{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1087af6d",
   "metadata": {},
   "source": [
    "# Linear Regression Using LinearSVR"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b0f118fd",
   "metadata": {},
   "source": [
    "In this notebook, we review how to create, train, and evaluate a linearSVR regression model using the Concrete ML library, our open-source, privacy-preserving, machine learning framework based on Fully Homomorphic Encryption (FHE).\n",
    "\n",
    "For the sake of simplicity, we will only consider a single explanatory variable, making it easy to plot its relationship with the target variable.  \n",
    "\n",
    "In order to identify the best set of hyperparameters for the linearSVR, we perform a grid search on the following : \n",
    "\n",
    "* $C$: (inverse) strength of the l2 penalization\n",
    "* $\\epsilon$: margin for the support vectors\n",
    "\n",
    "Please refer to Scikit-Learn documentation on [linearSVR](https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVR.html) for more details"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "244d272c",
   "metadata": {},
   "source": [
    "## Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9ea25662",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.datasets import load_diabetes\n",
    "from sklearn.metrics import make_scorer, mean_squared_error\n",
    "from sklearn.model_selection import GridSearchCV, KFold, train_test_split\n",
    "from sklearn.svm import LinearSVR as SklearnLinearSVR\n",
    "\n",
    "from concrete.ml.sklearn.svm import LinearSVR as ConcreteLinearSVR"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "728a5450",
   "metadata": {},
   "source": [
    "And some helpers for visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ba20b4ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import display\n",
    "\n",
    "train_plot_config = {\"c\": \"black\", \"marker\": \"D\", \"s\": 15, \"label\": \"Train data\"}\n",
    "test_plot_config = {\"c\": \"red\", \"marker\": \"x\", \"s\": 15, \"label\": \"Test data\"}\n",
    "\n",
    "\n",
    "def get_sklearn_plot_config(mse_score=None):\n",
    "    label = \"scikit-learn\"\n",
    "    if mse_score is not None:\n",
    "        label += f\", {'$MSE$'}={mse_score:.4f}\"\n",
    "    return {\"c\": \"blue\", \"linewidth\": 2.5, \"label\": label}\n",
    "\n",
    "\n",
    "def get_concrete_plot_config(mse_score=None):\n",
    "    label = \"Concrete ML\"\n",
    "    if mse_score is not None:\n",
    "        label += f\", {'$MSE$'}={mse_score:.4f}\"\n",
    "    return {\"c\": \"orange\", \"linewidth\": 2.5, \"label\": label}"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "6a8e092d",
   "metadata": {},
   "source": [
    "## Generate a data-set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9b2dc4be",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the diabetes data-set\n",
    "X, y = load_diabetes(return_X_y=True)\n",
    "# Use only one feature for educational purpose\n",
    "X = X[:, np.newaxis, 2]\n",
    "\n",
    "# We split the data-set into a training and a testing set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=23)\n",
    "\n",
    "# We sort the test set for a better visualization\n",
    "sorted_indexes = np.argsort(np.squeeze(X_test))\n",
    "X_test = X_test[sorted_indexes, :]\n",
    "y_test = y_test[sorted_indexes]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ef0975f5",
   "metadata": {},
   "source": [
    "We display the data-set to visualize the data distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ae52a4e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.ioff()\n",
    "\n",
    "plt.clf()\n",
    "fig, ax = plt.subplots(1, figsize=(10, 5))\n",
    "fig.patch.set_facecolor(\"white\")\n",
    "ax.scatter(X_train, y_train, **train_plot_config)\n",
    "ax.scatter(X_test, y_test, **test_plot_config)\n",
    "ax.legend()\n",
    "display(fig)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "931bce92",
   "metadata": {},
   "source": [
    "## Identify best set of hyperparameters"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "acbf941b",
   "metadata": {},
   "source": [
    "###  Sklearn LinearSVR."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "4860fd50",
   "metadata": {},
   "source": [
    "Create scorer with the [Mean Squared Error](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "11bb680b",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_scorer = make_scorer(mean_squared_error, greater_is_better=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f864d02e",
   "metadata": {},
   "source": [
    "Train the scikit-learn LinearSVR model on clear data.\n",
    "\n",
    "A parameter grid with several values for $\\epsilon$ and $C$ is used. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "449e78b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 16 candidates, totalling 80 fits\n"
     ]
    }
   ],
   "source": [
    "param_grid = {\n",
    "    \"epsilon\": [0.0, 1.0, 10.0, 20.0],\n",
    "    \"C\": [0.1, 100.0, 10000.0, 100000.0],\n",
    "}\n",
    "\n",
    "sklearn_rgs = SklearnLinearSVR()\n",
    "kfold_cv = KFold(n_splits=5, shuffle=True, random_state=13)\n",
    "\n",
    "gs_sklearn = GridSearchCV(\n",
    "    sklearn_rgs,\n",
    "    param_grid,\n",
    "    cv=kfold_cv,\n",
    "    scoring=grid_scorer,\n",
    "    verbose=1,\n",
    ").fit(X_train, y_train)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "5e3e7c24",
   "metadata": {},
   "source": [
    "###  Concrete ML quantized LinearSVR."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f82a8c7d",
   "metadata": {},
   "source": [
    "The typical development flow of a Concrete ML model is the following:\n",
    "\n",
    "* The model is trained on clear (plaintext) data, as only FHE inference is currently supported.\n",
    "* The resulting trained model is quantized using a `n_bits` parameter set by the user (see documentation [here](../built-in-models/linear.md#quantization-parameters)). This parameter can either be:\n",
    "    1. a dictionary composed of `op_inputs` and `op_weights` keys. These parameters are given as integers representing the number of bits over which the associated data should be quantized.\n",
    "    2. an integer, representing the number of bits over which each input and weight should be quantized. Default is 8. We try several values to test the various precisions gained for quantization. \n",
    "* The quantized model is compiled to an FHE-equivalent, following 3 steps:\n",
    "    1. Create an executable operation graph.\n",
    "    2. Check that the op-graph is FHE-compatible by checking the maximum bit-width needed to execute the model.\n",
    "    3. Determine cryptographic parameters that will help to generate the secret keys and evaluation keys. If no parameters can be found, the compilation process can't be completed and an error is thrown. The user then can either lower the value(s) chosen for `n_bits` or decrease the number of features found in the data-set (using techniques such as [PCA](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html)) and run the development flow once again.\n",
    "* Inference can then be done on encrypted data (using `fhe=\"execute\"` when calling `.predict()` method. See below)\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "2f6c5f40",
   "metadata": {},
   "source": [
    "We use the same grid of parameter values. We test several values for `n_bits` : `[6, 8, 12]` (see explanation in following sections). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e6126ebb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 48 candidates, totalling 240 fits\n"
     ]
    }
   ],
   "source": [
    "param_grid = {\n",
    "    \"n_bits\": [6, 8, 12],\n",
    "    \"epsilon\": [0.0, 1.0, 10.0, 20.0],\n",
    "    \"C\": [0.1, 100.0, 10000.0, 100000.0],\n",
    "}\n",
    "\n",
    "concrete_rgs = ConcreteLinearSVR()\n",
    "\n",
    "gs_concrete = GridSearchCV(\n",
    "    concrete_rgs,\n",
    "    param_grid,\n",
    "    cv=kfold_cv,\n",
    "    scoring=grid_scorer,\n",
    "    verbose=1,\n",
    ").fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4ac24379",
   "metadata": {},
   "outputs": [
    {
     "data": {
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79u1JSEhwP3c6nSxfvpxevXpRs2ZN9/JKlSrRt29fvvnmG/e+wsPD2bx5Mzt27DjrtvP7ea9evbpAd4wLiYiIoFu3bnz66aecOnUKyLsyP3/+fFq0aMEVV1zh3ke+nJwcTpw4Qe3atQkPD2fDhg1F2ucXX3yBn58f9957r3uZzWbj/vvvP+ux5Ttz5gzHjx/nqquuAijyfv+6/+J+n/zV369s5x9n/t/Pxx9/jMvl4vbbb+f48ePuR2xsLHXq1GHVqlUerw8ICGDQoEGFPlaAUaNGeSx/+OGHAQp8DtWoUYPExMSzbqt///4enwOtWrXCMAwGDx7s0a5Vq1YcOHDAY4aIv/4eHQ4Hx48fp3379uzevbtAd5+EhAT3OYa8Ozjq1q3rcb4/+ugjmjRpUuAuDfjfZ/aCBQuoX78+9erV8ziv1113HYD7vOZ/5i9evNhnZxYREXOp+BcRuUSBgYEFbsu12+1UrVq1QMFtt9vPWtTUqVPH43lISAiVKlXy6Ie8efNmbrrpJux2O2FhYVSsWJE77rgDwP2Pzl27dgHQsGHDSz6ufPv27QPwuG0W8gbGq1mzpnt9calWrZrH84iICIAiF4dQuPNcWDVq1GDUqFG89dZbVKhQgcTERF5//XWv9Pfft29fgfMNed0Q8tf/1cWco7CwMCDvi6Si+HuXlWPHjpGZmXnOvC6Xy93fe8KECaSlpXHFFVfQqFEjHn30UY9pHgMCAvjXv/7Fl19+SUxMjLsrTXJycqGy9evXj1OnTrF48WIgb+T8vXv3egz0d/r0acaOHUtcXBwBAQFUqFCBihUrkpaWVuTf3b59+6hUqRIhISEey892LlJSUnjggQeIiYkhKCiIihUrus/lxb5nSuJ98ld///upVasWVqvV/fezY8cODMOgTp06VKxY0eOxZcsW94Cg+apUqVLoQf327duH1WotMItIbGws4eHhBY717+/Tv/r7ecj/Ii8uLq7AcpfL5fH7+fbbb+nUqRPBwcGEh4dTsWJFd5/6v/8e/74fyDvnfz3fu3btuuDn9Y4dO9i8eXOBc5r/hVb+ef3nP/9JmzZtGDp0KDExMfTu3ZsPP/xQXwSIiJv6/IuIXKJzTU93ruXGeeYiP5e0tDTat29PWFgYEyZMoFatWgQGBrJhwwYef/zxMv2PO2+eR2976aWXGDhwIIsXL2b58uWMHDnS3R+6atWqJZbjYs5RWFgYlStXZtOmTUXa16WMmN6uXTt27drlPl9vvfUWr7zyCjNmzHDfPfPggw/SvXt3Fi1axLJly3j66aeZOHEiK1eupFmzZufd/o033ojdbmfu3Ln07duXuXPnYrPZ6N27t7vN/fffz6xZs3jwwQdp3bo1drsdi8VC7969i/Xv6Pbbb2ft2rU8+uijNG3alJCQEFwuF126dCmxv19v/y39/ctNl8uFxWLhyy+/POu+/v4lycW8lwp7B9P5tn2xn9m7du2iY8eO1KtXj5dffpm4uDj8/f354osveOWVVwr8Hr11vl0uF40aNeLll18+6/r8Ly2CgoJYs2YNq1at4vPPP2fp0qV88MEHXHfddSxfvtwnplIVEXOp+BcR8QE7duzg2muvdT/PyMjg8OHDdOvWDcgbrfvEiRN8/PHHHlOZ7dmzx2M7tWrVAmDTpk106tTpnPsrSheA+Ph4IG8Arb/e1p2dnc2ePXvOux9fc6HzfDYXOleNGjWiUaNGPPXUU6xdu5Y2bdowY8YMnnvuuYvOGR8fz7Zt2wosz+/ekf87uVQ33ngjM2fOZN26dbRu3fqitlGxYkXKly9/zrxWq9XjimpkZCSDBg1i0KBBZGRk0K5dO8aNG+fRdaZWrVo8/PDDPPzww+zYsYOmTZvy0ksv8Z///Oe8WQICArj11lt57733OHLkCAsWLOC6664jNjbW3WbhwoUMGDCAl156yb3szJkzZx2h/0Li4+NZsWIFGRkZHoXt389FamoqK1asYPz48R6Dx52t+0NR/zZL4n2Sb8eOHR5X1Hfu3InL5XIPxFirVi0Mw6BGjRruq9LeEh8fj8vlYseOHe47GyBv0MO0tDSvH+vZfPbZZ2RlZfHpp596XNX/e3eGoqhVq9YFv4CrVasWv/zyCx07drzg+8NqtdKxY0c6duzIyy+/zAsvvMCTTz7JqlWrStVntYgUD932LyLiA2bOnOkxRdn06dPJzc2la9euwP+uIP31ilF2djZvvPGGx3b+8Y9/UKNGDV599dUCxcxfXxscHAxQqIKnU6dO+Pv789prr3ls4+2338bhcHDDDTcU7iB9wIXO89mc61ylp6d79AWGvC8CrFZrgem3iqpbt258//33rFu3zr3s1KlTzJw5k+rVq3v0ub8Ujz32GMHBwQwdOpQjR44UWL9r1y73FJHnYrPZ6Ny5M4sXL/boPnHkyBHmzp3LNddc4+5i8Ndp7SDvSnDt2rXd5yszM7PA1HG1atUiNDS00Oe0X79+5OTkcPfdd3Ps2DGPW/7z8/79yuvUqVPPOmXmhXTr1o3c3FyPqd6cTidTp04tsE8oeMX31VdfLbDNovxtltT7JF/+FKH58o8z/+/n5ptvxmazMX78+ALHahhGgd9/UeR/Qff3c5Z/NbwkPofO9nt0OBzMmjXrord5yy238MsvvxSYreCv+7n99tv5448/ePPNNwu0OX36tHuMi5SUlALrmzZtCnDJn0kiUjboyr+IiA/Izs6mY8eO3H777Wzbto033niDa665hh49egB5U11FREQwYMAARo4cicVi4f333y/wD2yr1cr06dPp3r07TZs2ZdCgQVSqVImtW7eyefNmli1bBkDz5s0BGDlyJImJiQVujf6rihUrMmbMGMaPH0+XLl3o0aOHO+OVV17pHnegNLjQeT6bWrVqER4ezowZMwgNDSU4OJhWrVrxyy+/MGLECG677TauuOIKcnNzef/997HZbNxyyy2XlHP06NHMmzePrl27MnLkSCIjI3n33XfZs2cPH330kcfAi5eiVq1azJ07l3/+85/Ur1+f/v3707BhQ7Kzs1m7di0LFiwoMK/82Tz33HPu+cXvu+8+/Pz8+Pe//01WVhaTJk1yt0tISKBDhw40b96cyMhIfvzxRxYuXMiIESMA2L59u/v3k5CQgJ+fH5988glHjhw55/vz79q3b0/VqlVZvHgxQUFB3HzzzR7rb7zxRt5//33sdjsJCQmsW7eOr776iqioqMKfuD91796dNm3aMHr0aPbu3UtCQgIff/xxgb7fYWFh7vELcnJyqFKlCsuXLy9w5w7872/zySefpHfv3pQrV47u3bu7vxT4q5J6n+Tbs2cPPXr0oEuXLqxbt47//Oc/9O3blyZNmgB576fnnnuOMWPGsHfvXnr16kVoaCh79uzhk08+YdiwYTzyyCMXte8mTZowYMAAZs6c6e4G9f333/Puu+/Sq1cvjzt6ikvnzp3x9/ene/fu3H333WRkZPDmm28SHR3N4cOHL2qbjz76KAsXLuS2225j8ODBNG/enJSUFD799FNmzJhBkyZNuPPOO/nwww+55557WLVqFW3atMHpdLJ161Y+/PBDli1bRosWLZgwYQJr1qzhhhtuID4+nqNHj/LGG29QtWpVj0EhReQyVrKTC4iIlF7nmurv71NSGUbeNFYNGjQosDw+Pt5jqrP8bX799dfGsGHDjIiICCMkJMTo16+fceLECY/Xfvvtt8ZVV11lBAUFGZUrVzYee+wxY9myZQWmIzMMw/jmm2+M66+/3ggNDTWCg4ONxo0bG1OnTnWvz83NNe6//36jYsWKhsViKdS0f9OmTTPq1atnlCtXzoiJiTHuvffeAtMJFsdUf3+fSm/Pnj1nnXrvfIpynv8+1Z9hGMbixYuNhIQEw8/Pz73v3bt3G4MHDzZq1aplBAYGGpGRkca1115rfPXVVxd7yB527dpl3HrrrUZ4eLgRGBhotGzZ0liyZIlHG2+do+3btxt33XWXUb16dcPf398IDQ012rRpY0ydOtU9jaRh5E31N3z48LNuY8OGDUZiYqIREhJilC9f3rj22muNtWvXerR57rnnjJYtWxrh4eFGUFCQUa9ePeP55593T7t2/PhxY/jw4Ua9evWM4OBgw263G61atTI+/PDDQh1HvkcffdQAjNtvv73AutTUVGPQoEFGhQoVjJCQECMxMdHYunVrgWn0CjPVn2EYxokTJ4w777zTCAsLM+x2u3HnnXe6p4H86/k/ePCgcdNNNxnh4eGG3W43brvtNuPQoUMFpv80DMN49tlnjSpVqhhWq9Vj2r+/ZzSMknmf5P9d//7778att95qhIaGGhEREcaIESOM06dPF2j/0UcfGddcc40RHBxsBAcHG/Xq1TOGDx/unm7RMM79GWkY5/5czcnJMcaPH2/UqFHDKFeunBEXF2eMGTPG4z1qGAU/Zy90Hs722f7X4/7r59mnn35qNG7c2AgMDDSqV69u/Otf/zLeeeedAtMznivD2T5fTpw4YYwYMcKoUqWK4e/vb1StWtUYMGCAcfz4cXeb7Oxs41//+pfRoEEDIyAgwIiIiDCaN29ujB8/3nA4HIZhGMaKFSuMnj17GpUrVzb8/f2NypUrG3369DG2b99eIIeIXJ4shuEDIyaJiFymZs+ezaBBg/jhhx9o0aKF2XFERAoYN24c48eP59ixY1SoUMHsOCIicpHU519ERERERESkjFOffxERKbVOnz59wTnSIyMjCz2XuIiIiEhZpeJfRERKrQ8++IBBgwadt82qVavo0KFDyQQSERER8VHq8y8iIqXW4cOH2bx583nbNG/enIiIiBJKJCIiIuKbVPyLiIiIiIiIlHEa8E9ERERERESkjFOffy9xuVwcOnSI0NBQLBaL2XFERERERESkjDMMg5MnT1K5cmWs1vNf21fx7yWHDh0iLi7O7BgiIiIiIiJymTlw4ABVq1Y9bxsV/14SGhoK5J30sLAwk9OIiIiIiIhIWZeenk5cXJy7Hj0fFf9ekn+rf1hYmIp/ERERERERKTGF6XquAf9EREREREREyjgV/yIiIiIiIiJlnIp/ERERERERkTJOff5FRERERER8nGEY5OTkkJuba3YUKUF+fn6UK1fOK9PJq/gXERERERHxYVlZWezdu5eMjAyzo4gJQkJCqF69OgEBAZe0HRX/IiIiIiIiPsrlcvH777/j5+dHjRo1CAgI8MpVYPF9hmGQlZXFH3/8webNm6lfvz5BQUEXvT0V/yIiIiIiIj7qzJkzuFwuatSoQUhIiNlxpIQFBwfj7+/Ptm3b+Oijj+jQoQNVq1a9qG1pwD8REREREREfZ7WqdLtc5f/u09PT+fzzzzl8+PDFbceboURERERERETE+6Kjozlx4gQHDx68qNer+BcRERERERGfY7FYWLRo0TnX7927F4vFwsaNG0ssk5ksFgsWi4UzZ85c1OtV/IuIiIiIiFwGnC6DdbtOsHjjH6zbdQKnyzA70iWJi4vj8OHDNGzYEIDVq1djsVhIS0szNxgwe/ZsGjduTGBgINHR0QwfPtzsSBrwT0REREREpKxbuukw4z/7ncOO/101rmQP5JnuCXRpWMnEZBfPZrMRGxtrdowCXn75ZV566SUmT55Mq1atOHXqFHv37jU7lq78i4iIiIiIlGVLNx3m3v9s8Cj8AZIdZ7j3PxtYuuniBpC7kA4dOjBy5Egee+wxIiMjiY2NZdy4cUXaxuHDh+natStBQUHUrFmThQsXutf99bb/vXv3cu211wIQERGBxWJh4MCBACxcuJBGjRoRFBREVFQUnTp14tSpU946TA+pqak89dRTvPfee/Tt25datWrRuHFjevToUSz7KwoV/yIiIiIiIqWIYRhkZucW6nHyTA7PfLqZs93gn79s3Ke/c/JMzgW3ZRhF7ybw7rvvEhwczPr165k0aRITJkwgKSmp0K9/+umnueWWW/jll1/o168fvXv3ZsuWLQXaxcXF8dFHHwGwbds2Dh8+zJQpUzh8+DB9+vRh8ODBbNmyhdWrV3PzzTef91hCQkLO+7jnnnvO+dqkpCRcLhd//PEH9evXp2rVqtx+++0cOHCg0MdcXHTbv4iIiIiISClyOsdJwthlXtmWASSnn6HRuOUXbPv7hETK+xethGzcuDHPPPMMAHXq1GHatGmsWLGC66+/vlCvv+222xg6dCgAzz77LElJSUydOpU33njDo53NZiMyMhLIGxU/PDwcgF27dpGbm8vNN99MfHw8AI0aNTrvPi80gGBYWNg51+3evRuXy8ULL7zAlClTsNvtPPXUU1x//fX8+uuv+Pv7n3fbxUnFv4iIiIiIiBSLxo0bezyvVKkSR48eLfTrW7duXeB5UUb3b9KkCR07dqRRo0YkJibSuXNnbr31ViIiIs75mtq1axd6+3/ncrnIycnhtddeo3PnzgDMmzeP2NhYVq1aRWJi4kVv+1Kp+BcRERERESlFgsrZ+H1C4YrI7/ekMHDWDxdsN3vQlbSsEXnB/RZVuXLlPJ5bLBZcLleRt3OxbDYbSUlJrF27luXLlzN16lSefPJJ1q9fT40aNc76mpCQkPNu84477mDGjBlnXVepUt7giQkJCe5lFStWpEKFCuzfv/8ij8I7VPyLiIiIiIiUIhaLpdC337etU5FK9kCSHWfO2u/fAsTaA2lbpyI2q8WrOb3hu+++o3///h7PmzVrdta2+bfUO51Oj+UWi4U2bdrQpk0bxo4dS3x8PJ988gmjRo0663Yu5bb/Nm3aAHnjDlStWhWAlJQUjh8/7u52YBYV/yIiIiIiImWUzWrhme4J3PufDVjA4wuA/FL/me4JPln4AyxYsIAWLVpwzTXXMGfOHL7//nvefvvts7aNj4/HYrGwZMkSunXrRlBQEJs3b2bFihV07tyZ6Oho1q9fz7Fjx6hfv/4593kpt/1fccUV9OzZkwceeICZM2cSFhbGmDFjqFevnns2ArNotH8REREREZEyrEvDSky/4x/E2gM9lsfaA5l+xz/o0rCSSckubPz48cyfP5/GjRvz3nvvMW/ePI9b6v+qSpUqjB8/ntGjRxMTE8OIESMICwtjzZo1dOvWjSuuuIKnnnqKl156ia5duxZb5vfee49WrVpxww030L59e8qVK8fSpUsLdIEoaRbjYuZrkALS09Ox2+04HI7z3gYiIueXnZ3Foq//zdH0/USHVaNX+7vx9w8wO5aIiIiIKTIzM9myZQv169enfPnyl7Qtp8vg+z0pHD15hujQQFrWiPTZK/7yP/nvgb1797J9+3auueYa2rZtCxStDtVt/yLiM2YufpJ5xxdx3O/Pm5IyYPr7/6ZPhV4M6/m8ueFERERESjmb1ULrWlFmxxCT6LZ/kVIkOzuLD5NeY9pHj/Bh0mtkZ2eZHclrZi5+kmmpizlu8/z2+YTNwrTUxcxc/KRJyURERETE2+bMmUNISMhZHw0aNDA7XpmkK/+XkcvhduqyfIxl7ap4VvZpUhxHSU0/Roojmf8cX4Rhs4DFs/g3LBYshsH844sYmD22zPw+RURERC5nPXr0oFWrVmddZ3bf+LJKxf9loqwVjmdTlo8x/6q4cY6r4iymWI7R5XSSdvI4KenHSM84gePUCU6eTiXzjINTZxxkZp/kTM4pzuSeIiv3NFmuM2S7zpBtZJNt5JBFLjmWXLIsrj8fBlkWOG21kPO3Ih+/c9+IZFgsHPOzsOjrf3P79SO9fpwiIiIiUrJCQ0MJDQ01O8ZlxdTif+LEiXz88cds3bqVoKAgrr76av71r39Rt25dIG8+xGeeeYbly5ezf/9+KlasSK9evXj22Wex2+3u7Vj+XkQA8+bNo3fv3u7nq1evZtSoUWzevJm4uDieeuopBg4c6PGa119/ncmTJ5OcnEyTJk2YOnUqLVu2LJ6DL0FmFY4lqSwfY3Z2FvMKcVW84/6+nMo6SfqpFE6eOkHGaQeZWQ4ys9I5nZNBVu4pzuRmkuU8k1ekG1lkGzl5D4uTbJz/K9CtBmcscNpahJ5BVi7QkcjC/yaU+R+bYeBnGGQVYl9H0/cXPo+IiIiIiLiZWvx//fXXDB8+nCuvvJLc3FyeeOIJOnfuzO+//05wcDCHDh3i0KFD/N///R8JCQns27ePe+65h0OHDrFw4UKPbc2aNYsuXbq4n4eHh7t/3rNnDzfccAP33HMPc+bMYcWKFQwdOpRKlSqRmJgIwAcffMCoUaOYMWMGrVq14tVXXyUxMZFt27YRHR1dIuejOFyocMQw+M/xRVRYWRWbrXTeXuN05vD+iUUY1nMf4/snFuH3mT8WixWnKxeXkYvT5cIwnH8+d+J0OQEXTpcTl5H/MDDcP7tw4cJlODH+/NkwjLx1uP5cZrjX5f1sYPz5859LMDDytpv/s+UszzEwAAODTIuT4/4Xvirea1Xvc7bxYPvzcU5nL9IDXQaBhkGgy0KAYcHfsBCAjXKGH/74EWD1x98SgL81gABbIAG2YILKlSewXAjlA0Ip728nJCicsOBIwoIjsYdUINIeTXBgKAtXvs6zh968YPTosGqFO0YREREREfHgU1P9HTt2jOjoaL7++mvatWt31jYLFizgjjvu4NSpU/j55X13YbFY+OSTT+jVq9dZX/P444/z+eefs2nTJvey3r17k5aWxtKlSwFo1aoVV155JdOmTQPA5XIRFxfH/fffz+jRoy+Y3Ven+vsw6bVCFVVSNpQzjD+LdAgwLAS4rPhjxf/PAt3fUo4Aiz/+1gD8rYEE+pUnwK88QX7BBAWEUj7ATnCgndCgcMKCKxAWEklEWAUiQivi51d8Xw5lZ2eR+P4/OGGz5H1h8zcWw6CC02DpnRvU519EREQuK96c6k9KpzI51Z/D4QAgMjLyvG3CwsLchX++4cOHM3ToUGrWrMk999zDoEGD3N0B1q1bR6dOnTzaJyYm8uCDDwKQnZ3NTz/9xJgxY9zrrVYrnTp1Yt26dd44NNMU9jbp8FwXQUbpnOPztMUg7Tz9xfPF5rgIddmwYMECWLFg/fNnCxashoX8/1mxYLX8+ZMBVov1z7Z5y6wWC9b8Lfz5sxUrFkvecxtWLBabe5nV/bBhwYrN+ud6i8293GqxYbPYsFjz/mu12rBZ/dibspnFlu0XPL4xMQPo2+WRSz+hJvD3D6BPhV55XTQMw+MODsuf30/2rtBLhb+IiIiIyEXymeLf5XLx4IMP0qZNGxo2bHjWNsePH+fZZ59l2LBhHssnTJjAddddR/ny5Vm+fDn33XcfGRkZjByZNzBYcnIyMTExHq+JiYkhPT2d06dPk5qaitPpPGubrVu3njVLVlYWWVn/m2YtPT29yMdcEqLDqkHGhdvdX+3uUjuQWmHvbrgrvnQeY3Z2Ft8W4qr4rdfdb0I67xnW83lYzJ+DNv7vOCs4DXqXgUEbRURERETMVITRvIrX8OHD2bRpE/Pnzz/r+vT0dG644QYSEhIYN26cx7qnn36aNm3a0KxZMx5//HEee+wxJk+eXKx5J06ciN1udz/i4uKKdX8Xq1f7u6mQ63JfPf07i2FQMddFr/Z3l3Ay7ynrx5h/VRwocIxl7ar4sJ7Ps+zODQQ7XQAM8G/D0js3qPAXEREREblEPlH8jxgxgiVLlrBq1SqqVq1aYP3Jkyfp0qULoaGhfPLJJxec97FVq1YcPHjQfWU+NjaWI0eOeLQ5cuQIYWFhBAUFUaFCBWw221nbxMbGnnUfY8aMweFwuB8HDhwoyiGXmMuhcLwcjnFYz+cZEdGTKKfn8VVwGoyI6FmmimN//wCinXkfTdFh1Ur1701ERETEp7icsOe/8NvCvP+6nGYnOi+LxcKiRYvOuX7v3r1YLBY2btxYYplKM1OLf8MwGDFiBJ988gkrV66kRo0aBdqkp6fTuXNn/P39+fTTTwkMDLzgdjdu3EhERAQBAXlFQ+vWrVmxYoVHm6SkJFq3bg2Av78/zZs392jjcrlYsWKFu83fBQQEEBYW5vHwVZdD4Xi5HOOyOzfwdOW7uDskkacr31Vmr4rbXXl/u0fS95mcRERERKSM+P1TeLUhvHsjfDQk77+vNsxbXkrFxcVx+PBhd7fx1atXY7FYSEtLMzXXDz/8QMeOHQkPDyciIoLExER++eUXUzOByX3+hw8fzty5c1m8eDGhoaEkJycDYLfbCQoKchf+mZmZ/Oc//yE9Pd3dt75ixYrYbDY+++wzjhw5wlVXXUVgYCBJSUm88MILPPLI/wY+u+eee5g2bRqPPfYYgwcPZuXKlXz44Yd8/vnn7jajRo1iwIABtGjRgpYtW/Lqq69y6tQpBg0aVLInpZgM6/k8A7PHsujrf3M0fT/RYdXo1f7uMnVV9XI4Rn//gFI5bkFR2a0hQBYpZ45csK2IiIiIXMDvn8KH/YG/dZNNP5y3/Pb3IKGHKdEuhc1mO+ed2mbJyMigS5cu9OjRgzfeeIPc3FyeeeYZEhMTOXDgwAXvYi9Ophb/06dPB6BDhw4ey2fNmsXAgQPZsGED69evB6B27doebfbs2UP16tUpV64cr7/+Og899BCGYVC7dm1efvll7rrrLnfbGjVq8Pnnn/PQQw8xZcoUqlatyltvvUViYqK7zT//+U+OHTvG2LFjSU5OpmnTpixdurTAIICl2eVQOF4Ox3g5sPtFAidw5KSYHUVERETE9xgG5GQWrq3LCV8+RoHCP29DgAWWPg41O4DVdv5tlSvvMSvThXTo0IHGjRsTGBjIW2+9hb+/P/fcc0+BMdzO5/Dhw3Tt2pXVq1dTqVIlJk2axK233grk3fZfo0YNfv75Z8LDw7n22msBiIiIAGDAgAHMnj2bhQsXMn78eHbu3En58uVp1qwZixcvJjg4uNA5Cmvr1q2kpKQwYcIE97hwzzzzDI0bN2bfvn0F6tqSZGrxb5xjgLZ8HTp0uGCbLl260KVLlwvuq0OHDvz888/nbTNixAhGjBhxwW2JSPGKKl8JzuzAYZwyO4qIiIiI78nJhBcqe2ljBqQfghcLMYD5E4fAv2gF87vvvsuoUaNYv34969atY+DAgbRp04brr7++UK9/+umnefHFF5kyZQrvv/8+vXv35rfffqN+/foe7eLi4vjoo4+45ZZb2LZtm3t8t8OHD9OnTx8mTZrETTfdxMmTJ/nvf/973jozJCTkvJnuuOMOZsyYcdZ1devWJSoqirfffpsnnngCp9PJ22+/Tf369alevXqhjrm4+MxUfyIi+WLs8XAGHNZss6OIiIiIyCVo3LgxzzzzDAB16tRh2rRprFixotDF/2233cbQoUMBePbZZ0lKSmLq1Km88cYbHu1sNhuRkZEAREdHEx4eDsCuXbvIzc3l5ptvJj4+HoBGjRqdd58XGkDwfOO9hYaGsnr1anr16sWzzz4L5B33smXL8PMzt/xW8S8iPqdadF04Aik2l9lRRERERHxPufJ5V+ELY99amHPrhdv1WwjxV194v0XUuHFjj+eVKlXi6NGjhX793wdgb926dZFG92/SpAkdO3akUaNGJCYm0rlzZ2699VZ314CzuZRb80+fPs2QIUNo06YN8+bNw+l08n//93/ccMMN/PDDDwQFBV30ti+VT0z1JyLyV7XimgJw0mYl1XHM3DAiIiIivsZiybv9vjCPWtdBWGXgXH31LRBWJa/dhbZVhP7++f4+wJ3FYsHlKrkLPDabjaSkJL788ksSEhKYOnUqdevWZc+ePed8TUhIyHkf99xzzzlfO3fuXPbu3cusWbO48sorueqqq5g7dy579uxh8eLFxXGIhaYr/yLic2IjqxLkcnHaamXb/p+5qlFnsyOJiIiIlE5WG3T515+j/VvwHPjvz2K+y4sXHuzPJN999x39+/f3eN6sWbOztvX39wfA6XR6LLdYLLRp04Y2bdowduxY4uPj+eSTTxg1atRZt3Mpt/1nZmZitVqx/OWLkvznJfmlx9mo+BcRn2O12YhyWjhohf3JW1X8i4iIiFyKhB550/ktfTxvcL98YZXzCn8fnuZvwYIFtGjRgmuuuYY5c+bw/fff8/bbb5+1bXx8PBaLhSVLltCtWzeCgoLYvHkzK1asoHPnzkRHR7N+/XqOHTtWYMDAv7qU2/6vv/56Hn30UYYPH87999+Py+XixRdfxM/Pzz0bgVlU/IuIT7K7ynGQXJLTzn1LloiIiIgUUkIPqHdD3hgAGUcgJCavj7+PXvHPN378eObPn899991HpUqVmDdvHgkJCWdtW6VKFcaPH8/o0aMZNGgQ/fv35/HHH2fNmjW8+uqrpKenEx8fz0svvUTXrl2LJW+9evX47LPPGD9+PK1bt8ZqtdKsWTOWLl1KpUqVimWfhWUxLjSXnhRKeno6drsdh8Nx3ttARKRw7p7ZhrUB6dxMAuMHfGB2HBERERFTZGZmsmXLFurXr0/58kUfcE9Kv/z3wN69e9m+fTvXXHMNbdu2BYpWh2rAPxHxSXZbOABp2SfMDSIiIiIiUgao+BcRnxQRGA2Aw5VuchIRERER8bY5c+acczT9Bg0amB2vTFKffxHxSRVDq0Hqj6RbzpgdRURERES8rEePHrRq1eqs6/4+PaB4h4p/EfFJVaJqQyqk2pwXbiwiIiIipUpoaCihoaFmx7is6LZ/EfFJtao0AiDFZuFMVqbJaURERERESjcV/yLik2pWScDPMHBZLOw6+LvZcURERERESjUV/yLik/zK+RPhzJuJdPeh30xOIyIiIiJSuqn4FxGfFeHMG5bkcMpOk5OIiIiIiJRuKv5FxGfZCQTg2Mk/TE4iIiIiIlK6qfgXEZ9lt4YBkJp1xOQkIiIiIlLSLBYLixYtOuf6vXv3YrFY2LhxY4llKs1U/IuIzwr3rwCAw+kwOYmIiIhI6ed0Ofkh+Qe+2P0FPyT/gNNVuqdUjouL4/DhwzRs2BCA1atXY7FYSEtLMzXXyJEjad68OQEBATRt2rTA+tWrV9OzZ08qVapEcHAwTZs2Zc6cOcWey6/Y9yAicpGigqtAxiYcaKo/ERERkUvx1b6vePH7FzmS+b87KmPKxzC65Wg6xXcyMdnFs9lsxMbGmh3jrAYPHsz69ev59ddfC6xbu3YtjRs35vHHHycmJoYlS5bQv39/7HY7N954Y7Fl0pV/EfFZlcNrAuCw5pqcRERERKT0+mrfV4xaPcqj8Ac4mnmUUatH8dW+r4plvx06dGDkyJE89thjREZGEhsby7hx44q0jcOHD9O1a1eCgoKoWbMmCxcudK/7623/e/fu5dprrwUgIiICi8XCwIEDAVi4cCGNGjUiKCiIqKgoOnXqxKlTp7x1mAW89tprDB8+nJo1a551/RNPPMGzzz7L1VdfTa1atXjggQfo0qULH3/8cbFlAhX/IuLDqsUmAHDCBi5n6b4tTURERMRbDMMgMyezUI+TWSeZ+P1EDIyC2/nzfy9+/yIns05ecFuGUXAbF/Luu+8SHBzM+vXrmTRpEhMmTCApKanQr3/66ae55ZZb+OWXX+jXrx+9e/dmy5YtBdrFxcXx0UcfAbBt2zYOHz7MlClTOHz4MH369GHw4MFs2bKF1atXc/PNN5/3WEJCQs77uOeee4p8Hi7E4XAQGRnp9e3+lW77FxGfVSe+MfwIWVYLfxzdTVylOmZHEhERETHd6dzTtJrbymvbO5J5hKvnX33Bduv7rqd8ufJF2nbjxo155plnAKhTpw7Tpk1jxYoVXH/99YV6/W233cbQoUMBePbZZ0lKSmLq1Km88cYbHu1sNpu7eI6OjiY8PByAXbt2kZuby80330x8fDwAjRo1Ou8+LzSAYFhYWKGyF9aHH37IDz/8wL///W+vbvfvVPyLiM+yh0Rid7pw2KzsPPirin8RERGRUqZx48YezytVqsTRo0cL/frWrVsXeF6U0f2bNGlCx44dadSoEYmJiXTu3Jlbb72ViIiIc76mdu3ahd7+pVq1ahWDBg3izTffpEGDBsW6LxX/IuLTIpxWHDY4cGy72VFEREREfEKQXxDr+64vVNufjvzEfSvuu2C7Nzq+QfOY5hfcb1GVK1fO47nFYsHlchV5OxfLZrORlJTE2rVrWb58OVOnTuXJJ59k/fr11KhR46yvCQkJOe8277jjDmbMmHHJ2b7++mu6d+/OK6+8Qv/+/S95exei4l9EfFq4KwDI4mj6PrOjiIiIiPgEi8VS6Nvvr658NTHlYziaefSs/f4tWIgpH8PVla/GZrV5O+ol++677zwK4++++45mzZqdta2/vz8Azr+NFWWxWGjTpg1t2rRh7NixxMfH88knnzBq1KizbqckbvtfvXo1N954I//6178YNmzYJW+vMFT8i4hPC7OEAFmk/G10WhERERG5MJvVxuiWoxm1ehQWLB5fAFiwAPB4y8d9svAHWLBgAS1atOCaa65hzpw5fP/997z99ttnbRsfH4/FYmHJkiV069aNoKAgNm/ezIoVK+jcuTPR0dGsX7+eY8eOUb9+/XPu81Jv+9+5cycZGRkkJydz+vRp95cJCQkJ+Pv7s2rVKm688UYeeOABbrnlFpKTk4G8Ly+Kc9A/jfYvIj4tvFxef6y03BSTk4iIiIiUTp3iO/Fyh5eJLh/tsTymfAwvd3iZTvGdTEp2YePHj2f+/Pk0btyY9957j3nz5pGQkHDWtlWqVGH8+PGMHj2amJgYRowYQVhYGGvWrKFbt25cccUVPPXUU7z00kt07dq12DIPHTqUZs2a8e9//5vt27fTrFkzmjVrxqFDh4C8GRAyMzOZOHEilSpVcj9uvvnmYssEYDEuZr4GKSA9PR273Y7D4fD66I8il7OXP7iPWWf+S5Mz/vzn7p/MjiMiIiJSojIzM9myZQv169enfPmijbT/d06Xkw1HN3As8xgVy1fkH9H/8Nkr/vI/+e+BvXv3sn37dq655hratm0LFK0O1W3/IuLTosPi4cx/SbNmmx1FREREpFSzWW1cGXul2THEJLrtX0R8Wlx0XQBSbSU3KqyIiIiIFK85c+YQEhJy1kdxT3l3udKVfxHxabXimsImSLdZSTt5nPDQCmZHEhEREZFL1KNHD1q1anXWdX+fHlC8Q8W/iPi0ylFxBLoMzlgtbN/3Cy0bdjQ7koiIiIhcotDQUEJDQ82OcVnRbf8i4tOsNhtRf07Vui95i7lhREREREzicqkL5OXKW797Ff8i4vPCXXm3fiWn7TY5iYiIiEjJ8vf3ByAjI8PkJGKW/N99Tk7OJW1Ht/2LiM+zUx5I58SpQ2ZHERERESlRfn5+VKhQgT/++AOAkJAQrFZdw70cuFwuMjIy+OOPP0hLS7vkOwBU/IuIzwuzhQPppGUfNzuKiIiISImrVq0agPsLALm8pKWlceTIEfdzP7+LK+NV/IuIz4sMrAi5+3G4TpodRURERKTEWSwW4uPjOXr0KOvXr8dut1O+fHmzY0kJyMnJcV/xT01Nxd/fn8jIyIvalop/EfF5FUOrQepPOCynzY4iIiIiYppmzZrhcDj4+eefcTqdZseREmQYBgEBAbRt25Yrrrjiorah4l9EfF7lyNqQCqk2/Z+ciIiIXL78/Pzo0KED8fHxZGRkaAaAy4ifnx9hYWFUr14di8VycdvwciYREa+rVaUh7IJUm4WsrDMEBASaHUlERETEFH5+ftSpU8fsGFIKaZhIEfF5Nao0wGYYOC0Wdv2x2ew4IiIiIiKljop/EfF5/v4BRDoNAHYf2mRyGhERERGR0kfFv4iUChFOGwCHTuw0OYmIiIiISOmj4l9ESoUwggA4dvKAyUlEREREREofFf8iUiqEW8MASMs6anISEREREZHSR8W/iJQK4f5RADicDpOTiIiIiIiUPir+RaRUiCxfGQAHmSYnEREREREpfVT8i0ipUCmiJgBp1hyTk4iIiIiIlD4q/kWkVKgemwDACRu4nE6T04iIiIiIlC4q/kWkVKhTrQkAWVYLfxzbb3IaEREREZHSRcW/iJQK9tAo7E4XADsP/mxyGhERERGR0kXFv4iUGhHOvI+sA8e2m5xERERERKR0UfEvIqVGuMsfgGOOfSYnEREREREpXVT8i0ipEWYNAeDE6WSTk4iIiIiIlC4q/kWk1Aj3iwTAkZtqchIRERERkdJFxb+IlBqRQbEAOFwZJicRERERESldVPyLSKkRbY8HwGHNMjmJiIiIiEjpouJfREqNuIpXAJBqc5mcRERERESkdFHxLyKlRq24pgA4bFbSTp4wN4yIiIiISCmi4l9ESo0qFeIJdOVd9d+x/xeT04iIiIiIlB4q/kWk1LDabEQ5LQDsS95ichoRERERkdJDxb+IlCp2VzkADqftNjmJiIiIiEjpoeJfREoVO+UBSDn1h8lJRERERERKDxX/IlKq2G12AFKzNOCfiIiIiEhhqfgXkVIlMjAaAIcr3eQkIiIiIiKlh6nF/8SJE7nyyisJDQ0lOjqaXr16sW3bNo82Z86cYfjw4URFRRESEsItt9zCkSNHPNrs37+fG264gfLlyxMdHc2jjz5Kbm6uR5vVq1fzj3/8g4CAAGrXrs3s2bML5Hn99depXr06gYGBtGrViu+//97rxywil6ZCSFUAHJYzJicRERERESk9TC3+v/76a4YPH853331HUlISOTk5dO7cmVOnTrnbPPTQQ3z22WcsWLCAr7/+mkOHDnHzzTe71zudTm644Qays7NZu3Yt7777LrNnz2bs2LHuNnv27OGGG27g2muvZePGjTz44IMMHTqUZcuWudt88MEHjBo1imeeeYYNGzbQpEkTEhMTOXr0aMmcDBEplMpRtQFIs+VeoKWIiIiIiOSzGIZhmB0i37Fjx4iOjubrr7+mXbt2OBwOKlasyNy5c7n11lsB2Lp1K/Xr12fdunVcddVVfPnll9x4440cOnSImJgYAGbMmMHjjz/OsWPH8Pf35/HHH+fzzz9n06ZN7n317t2btLQ0li5dCkCrVq248sormTZtGgAul4u4uDjuv/9+Ro8efcHs6enp2O12HA4HYWFh3j41IvKnLbt/5Pb/DsJmGHzf9yf8/QPMjiQiIiIiYoqi1KE+1eff4XAAEBkZCcBPP/1ETk4OnTp1crepV68e1apVY926dQCsW7eORo0auQt/gMTERNLT09m8ebO7zV+3kd8mfxvZ2dn89NNPHm2sViudOnVyt/m7rKws0tPTPR4iUvxqVW2IzTBwWizsOrjZ7DgiIiIiIqWCzxT/LpeLBx98kDZt2tCwYUMAkpOT8ff3Jzw83KNtTEwMycnJ7jZ/Lfzz1+evO1+b9PR0Tp8+zfHjx3E6nWdtk7+Nv5s4cSJ2u939iIuLu7gDF5Ei8fcPJMKZd8PSnkMq/kVERERECsNniv/hw4ezadMm5s+fb3aUQhkzZgwOh8P9OHDggNmRRC4bEU4bAH+c2GFyEhERERGR0sHP7AAAI0aMYMmSJaxZs4aqVau6l8fGxpKdnU1aWprH1f8jR44QGxvrbvP3UfnzZwP4a5u/zxBw5MgRwsLCCAoKwmazYbPZztomfxt/FxAQQECA+hqLmMFuBAGZHMvQl24iIiIiIoVh6pV/wzAYMWIEn3zyCStXrqRGjRoe65s3b065cuVYsWKFe9m2bdvYv38/rVu3BqB169b89ttvHqPyJyUlERYWRkJCgrvNX7eR3yZ/G/7+/jRv3tyjjcvlYsWKFe42IuI77NZQAFLPaDYOEREREZHCMPXK//Dhw5k7dy6LFy8mNDTU3b/ebrcTFBSE3W5nyJAhjBo1isjISMLCwrj//vtp3bo1V111FQCdO3cmISGBO++8k0mTJpGcnMxTTz3F8OHD3Vfm77nnHqZNm8Zjjz3G4MGDWblyJR9++CGff/65O8uoUaMYMGAALVq0oGXLlrz66qucOnWKQYMGlfyJEZHzCvevABzB4UwzO4qIiIiISKlgavE/ffp0ADp06OCxfNasWQwcOBCAV155BavVyi233EJWVhaJiYm88cYb7rY2m40lS5Zw77330rp1a4KDgxkwYAATJkxwt6lRowaff/45Dz30EFOmTKFq1aq89dZbJCYmutv885//5NixY4wdO5bk5GSaNm3K0qVLCwwCKCLmiwquBKc24yDT7CgiIiIiIqWCxTAMw+wQZUFR5lcUkUuz8KupjP9jJlVyDJYO3WR2HBERERERUxSlDvWZ0f5FRAorPrY+ACds4HI6TU4jIiIiIuL7VPyLSKlTJ74pAGesFg6d0Ij/IiIiIiIXouJfREqd8NAKhDldAOw68IvJaUREREREfJ+KfxEplSKceR9fB45tMzmJiIiIiIjvU/EvIqVSuMsfgCOOveYGEREREREpBVT8i0ipZLcEA5CSmWxyEhERERER36fiX0RKJbtfJABpOSkmJxERERER8X0q/kWkVIoqHwtAupFhchIREREREd+n4l9ESqWKYdUASLNmmZxERERERMT3qfgXkVIprmJdAFJtLpOTiIiIiIj4PhX/IlIq1anaGACHzYojI9XkNCIiIiIivk3Fv4iUSpWjaxLgMgDYse8Xk9OIiIiIiPg2Ff8iUipZbTainHk/70v+3dwwIiIiIiI+TsW/iJRa4S4/AA6n7TY5iYiIiIiIb1PxLyKlVhjlAThx6pDJSUREREREfJuKfxEptew2OwCp2cdMTiIiIiIi4ttU/ItIqRUZEA2Aw5VuchIREREREd+m4l9ESq0KIVUBSOeMyUlERERERHybin8RKbUqR9UGINWWa3ISERERERHfpuJfREqtGpUaApBqs5Cdk21yGhERERER36XiX0RKrdrVGmI1DHItFvb88bvZcUREREREfJaKfxEptQL8g4h0GgDs+mOTyWlERERERHyXin8RKdUinDYADp3YYXISERERERHfpeJfREo1uxEIwLGTB0xOIiIiIiLiu1T8i0ipFmYNAyDlzFGTk4iIiIiI+C4V/yJSqoX7RwHgcKaZG0RERERExIep+BeRUq1C+coApBunTE4iIiIiIuK7VPyLSKkWG1EDAIctx+QkIiIiIiK+S8W/iJRq1aLrAXDCZuByuUxOIyIiIiLim1T8i0ipVje+KQCnrVYOnzhobhgRERERER+l4l9ESrVwezShzrwr/jv3bzQ3jIiIiIiIj1LxLyKlXqQz76PswLFtJicREREREfFNKv5FpNQLd/kDcNSxz+QkIiIiIiK+ScW/iJR6YZZgAE5kHjI5iYiIiIiIb1LxLyKlXni5CADSclJNTiIiIiIi4ptU/ItIqRcZGAtAunHS5CQiIiIiIr5Jxb+IlHrRYdUAcFizTE4iIiIiIuKbVPyLSKlXtWJdAFJtLpOTiIiIiIj4JhX/IlLq1a7aCIA0m5X0jDRzw4iIiIiI+CAV/yJS6lWNqU2AywBgx/5fTU4jIiIiIuJ7VPyLSKlntdmIdOb9vDd5s7lhRERERER8kIp/ESkTwl1+ABxO3W1yEhERERER36PiX0TKBDvlAThx6pDJSUREREREfI+KfxEpE+w2OwBp2cdMTiIiIiIi4ntU/ItImRARUBEAhzPd5CQiIiIiIr5Hxb+IlAkVQ6oA4LCcNjmJiIiIiIjvUfEvImVCpchaAKTZck1OIiIiIiLie1T8i0iZULNyIwBO2Czk5uaYnEZERERExLeo+BeRMqFW1YZYDQOnxcKuPzabHUdERERExKeo+BeRMiEwMJgIpwHAHhX/IiIiIiIeVPyLSJkR4bQBcPD4DpOTiIiIiIj4FhX/IlJm2I0AAI6dPGByEhERERER36LiX0TKDLs1DIDUrKMmJxERERER8S0q/kWkzAj3jwLAkZtqchIREREREd+i4l9Eyoyo8pUAcJBpchIREREREd+i4l9EyozY8BoApFmzTU4iIiIiIuJbVPyLSJlRLaYeAKk2A8MwTE4jIiIiIuI7VPyLSJlxRbWmAGRarSSfOGhuGBERERERH6LiX0TKjMjwWEKdLgB27N9obhgRERERER+i4l9EypRIpwWAA8e2mZxERERERMR3qPgXkTLF7vIHIDltr7lBRERERER8iIp/ESlTwizBAKRkHjY5iYiIiIiI71DxLyJlSrhfJACO3BSTk4iIiIiI+A4V/yJSpkQGxQDgcGWYnERERERExHeYWvyvWbOG7t27U7lyZSwWC4sWLfJYb7FYzvqYPHmyu0316tULrH/xxRc9tvPrr7/Stm1bAgMDiYuLY9KkSQWyLFiwgHr16hEYGEijRo344osviuWYRaR4xYRWA8BhPWNyEhERERER32Fq8X/q1CmaNGnC66+/ftb1hw8f9ni88847WCwWbrnlFo92EyZM8Gh3//33u9elp6fTuXNn4uPj+emnn5g8eTLjxo1j5syZ7jZr166lT58+DBkyhJ9//plevXrRq1cvNm3aVDwHLiLFpkrFKwBItblMTiIiIiIi4jv8zNx5165d6dq16znXx8bGejxfvHgx1157LTVr1vRYHhoaWqBtvjlz5pCdnc0777yDv78/DRo0YOPGjbz88ssMGzYMgClTptClSxceffRRAJ599lmSkpKYNm0aM2bMuJRDFJESVqtqI9gKaTYrJzMdhJa3mx1JRERERMR0pabP/5EjR/j8888ZMmRIgXUvvvgiUVFRNGvWjMmTJ5Obm+tet27dOtq1a4e/v797WWJiItu2bSM1NdXdplOnTh7bTExMZN26defMk5WVRXp6usdDRMxXLfYK/F0GADv2/2pyGhERERER31Bqiv93332X0NBQbr75Zo/lI0eOZP78+axatYq7776bF154gccee8y9Pjk5mZiYGI/X5D9PTk4+b5v89WczceJE7Ha7+xEXF3dJxyci3mG12Yhy5v289/Dv5oYREREREfERpt72XxTvvPMO/fr1IzAw0GP5qFGj3D83btwYf39/7r77biZOnEhAQECx5RkzZozHvtPT0/UFgIiPCHf5cRgnh1N3mR1FRERERMQnlIri/7///S/btm3jgw8+uGDbVq1akZuby969e6lbty6xsbEcOXLEo03+8/xxAs7V5lzjCAAEBAQU65cLInLx7EYQkMHxjD/MjiIiIiIi4hNKxW3/b7/9Ns2bN6dJkyYXbLtx40asVivR0dEAtG7dmjVr1pCTk+Nuk5SURN26dYmIiHC3WbFihcd2kpKSaN26tRePQkRKit2WN8hfWvZxk5OIiIiIiPgGU4v/jIwMNm7cyMaNGwHYs2cPGzduZP/+/e426enpLFiwgKFDhxZ4/bp163j11Vf55Zdf2L17N3PmzOGhhx7ijjvucBf2ffv2xd/fnyFDhrB582Y++OADpkyZ4nHL/gMPPMDSpUt56aWX2Lp1K+PGjePHH39kxIgRxXsCRKRYRARUBMDhdJicRERERETEN5h62/+PP/7Itdde636eX5APGDCA2bNnAzB//nwMw6BPnz4FXh8QEMD8+fMZN24cWVlZ1KhRg4ceesijsLfb7Sxfvpzhw4fTvHlzKlSowNixY93T/AFcffXVzJ07l6eeeoonnniCOnXqsGjRIho2bFhMRy4ixalCSFVI34jDctrsKCIiIiIiPsFiGIZhdoiyID09HbvdjsPhICwszOw4Ipe1z75+iyf2TiEm1+CrIZvMjiMiIiIiUiyKUoeWij7/IiJFUb1yAwCO2yA3N+cCrUVEREREyj4V/yJS5tSOa4zFMHBaLOw6tMXsOCIiIiIiplPxLyJlTlBgMJHOvB5New5uNjmNiIiIiIj5VPyLSJkU4bIBcPDEdpOTiIiIiIiYT8W/iJRJdlcAAMdP7r9ASxERERGRsk/Fv4iUSXZrKAApZ46anERERERExHwq/kWkTLL7RwHgyE01OYmIiIiIiPlU/ItImVShfCUA0sk0OYmIiIiIiPlU/ItImRRjrw5AmjXb3CAiIiIiIj5Axb+IlEnVYuoDkGIzTE4iIiIiImI+Ff8iUibVqdYYgEyrleQTB01OIyIiIiJiLhX/IlImVYioQojTBcCO/b+YnEZERERExFwq/kWkzIp0WgA4cHSryUlERERERMyl4l9Eyiy74Q9AsmOvuUFEREREREym4l9Eyiw7wQCknDpschIREREREXOp+BeRMivcLwIAR26KyUlERERERMyl4l9EyqyIwBgAHK6TJicRERERETGXin8RKbNiwqoB4LBmmZxERERERMRcKv5FpMyqUuEKAFJtTpOTiIiIiIiYS8W/iJRZtao0BCDVZiUjU7f+i4iIiMjlS8W/iJRZ8ZXrUs4wANhx4BeT04iIiIiImEfFv4iUWVabH1G5ecX/nsO/m5xGRERERMQ8Kv5FpEyLcPkBkJyyy+QkIiIiIiLmUfEvImVaGOUBOH7qD5OTiIiIiIiYR8W/iJRpdmsYAGlZx0xOIiIiIiJiHhX/IlKmRQRUBMDhSjc5iYiIiIiIeVT8i0iZViGkKgAOTpucRERERETEPCr+RaRMqxxZC4A0a67JSUREREREzFPk4n/p0qV888037uevv/46TZs2pW/fvqSmpno1nIjIpaoeWx+AE36Qm6svAERERETk8lTk4v/RRx8lPT2v7+xvv/3Gww8/TLdu3dizZw+jRo3yekARkUtRO74pFsMg12Jhz6HtZscRERERETGFX1FfsGfPHhISEgD46KOPuPHGG3nhhRfYsGED3bp183pAEZFLUT4whAinQYqfhT2HfqVOtQSzI4mIiIiIlLgiX/n39/cnMzMTgK+++orOnTsDEBkZ6b4jQETEl0Q4bQAcPKYr/yIiIiJyeSrylf9rrrmGUaNG0aZNG77//ns++OADALZv307VqlW9HlBE5FKFGwHAGY6ePGB2FBERERERUxT5yv+0adPw8/Nj4cKFTJ8+nSpVqgDw5Zdf0qVLF68HFBG5VGHWEABSziSbnERERERExBxFvvJfrVo1lixZUmD5K6+84pVAIiLeZi8XBRzHkZtmdhQREREREVMUqvgvSl/+sLCwiw4jIlIcKpSvBKe3kW6cMjuKiIiIiIgpClX8h4eHY7FYCrVBp9N5SYFERLwtJrwGnF6Nw5pjdhQREREREVMUqvhftWqV++e9e/cyevRoBg4cSOvWrQFYt24d7777LhMnTiyelCIilyA+uh4chhQ/l9lRRERERERMUajiv3379u6fJ0yYwMsvv0yfPn3cy3r06EGjRo2YOXMmAwYM8H5KEZFLUCe+MfwCp6xWkk8cIjaqstmRRERERERKVJFH+1+3bh0tWrQosLxFixZ8//33XgklIuJNFSKqEuzKu+q/8+AvJqcRERERESl5RS7+4+LiePPNNwssf+utt4iLi/NKKBERb4vKzRu35MCRrSYnEREREREpeUWe6u+VV17hlltu4csvv6RVq1YAfP/99+zYsYOPPvrI6wFFRLzB7vIHcjictsfsKCIiIiIiJa7IV/67devG9u3b6d69OykpKaSkpNC9e3e2b99Ot27diiOjiMgls1uCAUjNPGxyEhERERGRklfkK/+Qd+v/Cy+84O0sIiLFJtwvHEgjLeeE2VFEREREREpcoYr/X3/9tdAbbNy48UWHEREpLhGBMZCzF4frpNlRRERERERKXKGK/6ZNm2KxWDAM47ztLBYLTqfTK8FERLwpOiwOTqwn3ZJldhQRERERkRJXqOJ/zx4NkCUipVuVqDpwAlJs+oJSRERERC4/hSr+4+PjizuHiEixql21MWyHNJuFU5kZBJcPMTuSiIiIiEiJKfJo/wC7du3i/vvvp1OnTnTq1ImRI0eya9cub2cTEfGa+Mr1KGcYGBYL2w/8YnYcEREREZESVeTif9myZSQkJPD999/TuHFjGjduzPr162nQoAFJSUnFkVFE5JJZbX5E5uaNW7Lv8BaT04iIiIiIlKwiT/U3evRoHnroIV588cUCyx9//HGuv/56r4UTEfGmCJcfR3BxOFV3KomIiIjI5aXIV/63bNnCkCFDCiwfPHgwv//+u1dCiYgUBztBABzLOGhyEhERERGRklXk4r9ixYps3LixwPKNGzcSHR3tjUwiIsUizBoGQFrWMZOTiIiIiIiUrCLf9n/XXXcxbNgwdu/ezdVXXw3At99+y7/+9S9GjRrl9YAiIt4SEVARXIdxuBxmRxERERERKVFFLv6ffvppQkNDeemllxgzZgwAlStXZty4cYwcOdLrAUVEvKVCcFU4+SsOTpsdRURERESkRBXqtv9PP/2UnJwcACwWCw899BAHDx7E4XDgcDg4ePAgDzzwABaLpVjDiohcisqRNQFwWHNNTiIiIiIiUrIKVfzfdNNNpKWlAWCz2Th69CgAoaGhhIaGFls4ERFvql4pAYATfuDMdZqcRkRERESk5BSq+K9YsSLfffcdAIZh6Aq/iJRKdeKaAJBjsbA3ebvJaURERERESk6hiv977rmHnj17YrPZsFgsxMbGYrPZzvoQEfFV5cuHEZnrAmDXwV9NTiMiIiIiUnIKNeDfuHHj6N27Nzt37qRHjx7MmjWL8PDwYo4mIuJ94S4rKcDB4zvMjiIiIiIiUmIKPdp/vXr1qFevHs888wy33XYb5cuXL85cIiLFItwVCJzhWPp+s6OIiIiIiJSYIk/198wzzxRHDhGREhFmDQHOkHLmiNlRRERERERKTKH6/IuIlBXh5SIBcOSmmpxERERERKTkqPgXkctKVFBlABxGhslJRERERERKjqnF/5o1a+jevTuVK1fGYrGwaNEij/UDBw7EYrF4PLp06eLRJiUlhX79+hEWFkZ4eDhDhgwhI8PzH/W//vorbdu2JTAwkLi4OCZNmlQgy4IFC6hXrx6BgYE0atSIL774wuvHKyLmiw2PB8BhzTE5iYiIiIhIySl08T9t2jTS0tK8uvNTp07RpEkTXn/99XO26dKlC4cPH3Y/5s2b57G+X79+bN68maSkJJYsWcKaNWsYNmyYe316ejqdO3cmPj6en376icmTJzNu3DhmzpzpbrN27Vr69OnDkCFD+Pnnn+nVqxe9evVi06ZNXj1eETFfXEw9AFJtLpOTiIiIiIiUHIthGEZhGtrtdnJycujVqxdDhw7luuuu824Qi4VPPvmEXr16uZcNHDiQtLS0AncE5NuyZQsJCQn88MMPtGjRAoClS5fSrVs3Dh48SOXKlZk+fTpPPvkkycnJ+Pv7AzB69GgWLVrE1q1bAfjnP//JqVOnWLJkiXvbV111FU2bNmXGjBmFyp+eno7dbsfhcBAWFnYRZ0BESsLR4wfp+HlXAFb0TCI6PNbkRCIiIiIiF6codWihr/wnJyczY8YMDh8+zPXXX0+NGjV49tlnOXDgwCUHPp/Vq1cTHR1N3bp1uffeezlx4oR73bp16wgPD3cX/gCdOnXCarWyfv16d5t27dq5C3+AxMREtm3bRmpqqrtNp06dPPabmJjIunXrzpkrKyuL9PR0j4eI+L7oqCqUd+Vd9d+xb6O5YURERERESkihi/+goCD69+/PqlWr2LFjB3feeSdvv/02NWrUoEuXLixYsICcHO/2oe3SpQvvvfceK1as4F//+hdff/01Xbt2xel0AnlfSERHR3u8xs/Pj8jISJKTk91tYmJiPNrkP79Qm/z1ZzNx4kTsdrv7ERcXd2kHKyIlw2IhKtcCwP4jW0wOIyIiIiJSMi5qwL+aNWsyYcIE9uzZw5dffklUVBQDBw6kSpUqXg3Xu3dvevToQaNGjejVqxdLlizhhx9+YPXq1V7dz8UYM2YMDofD/SjuOyBExHvCjXIAHHHsNTeIiIiIiEgJuaTR/i0WC35+flgsFgzD8PqV/7+rWbMmFSpUYOfOnQDExsZy9OhRjza5ubmkpKQQGxvrbnPkyBGPNvnPL9Qmf/3ZBAQEEBYW5vEQkdLBTjAAJzIPm5xERERERKRkXFTxf+DAASZMmEDNmjW5/vrrOXToEG+++SaHDxfvP6QPHjzIiRMnqFSpEgCtW7cmLS2Nn376yd1m5cqVuFwuWrVq5W6zZs0ajy8mkpKSqFu3LhEREe42K1as8NhXUlISrVu3LtbjERFz2P3CAUjLPnH+hiIiIiIiZUShi//s7Gzmz59P586dqVGjBm+++SZ9+/Zl+/btrFy5kn79+hEYGFiknWdkZLBx40Y2btwIwJ49e9i4cSP79+8nIyODRx99lO+++469e/eyYsUKevbsSe3atUlMTASgfv36dOnShbvuuovvv/+eb7/9lhEjRtC7d28qV64MQN++ffH392fIkCFs3ryZDz74gClTpjBq1Ch3jgceeIClS5fy0ksvsXXrVsaNG8ePP/7IiBEjinQ8IlI6RAbmjfGR7jppchIRERERkZLhV9iGsbGxnDp1iu7du/PZZ5+RmJiI1XpJvQb48ccfufbaa93P8wvyAQMGMH36dH799Vfeffdd0tLSqFy5Mp07d+bZZ58lICDA/Zo5c+YwYsQIOnbsiNVq5ZZbbuG1115zr7fb7Sxfvpzhw4fTvHlzKlSowNixYxk2bJi7zdVXX83cuXN56qmneOKJJ6hTpw6LFi2iYcOGl3R8IuKbKobGQcr3OCxnzI4iIiIiIlIiLIZhGIVp+PLLL3PnnXdSsWLF4s5UKhVlfkURMdeytf/hkR3/IjLXxddDNpsdR0RERETkohSlDi30pftBgwYxf/78s85n73A4mDp1qua6F5FSoXbVxgCk2iycOnPK5DQiIiIiIsWv0MX/tGnTWLNmzVm/TbDb7fz3v/9l6tSpXg0nIlIcqlepj59hYFgs7Nz/q9lxRERERESKXaGL/48++oh77rnnnOvvvvtuFi5c6JVQIiLFyWYrR5Qzr8fT3mTd9i8iIiIiZV+hi/9du3ZRp06dc66vU6cOu3bt8kooEZHiFuHMG+/00Al9bomIiIhI2Vfo4t9ms3Ho0KFzrj906NAlj/4vIlJSwowgAI5nHDQ5iYiIiIhI8St0td6sWTMWLVp0zvWffPIJzZo180YmEZFiZ7fljV+SmnXc5CQiIiIiIsXPr7ANR4wYQe/evalatSr33nsvNpsNAKfTyRtvvMErr7zC3Llziy2oiIg3RfhXAOMwDlea2VFERERERIpdoYv/W265hccee4yRI0fy5JNPUrNmTQB2795NRkYGjz76KLfeemuxBRUR8aYKIVXg5G+kc9rsKCIiIiIixa7QxT/A888/T8+ePZkzZw47d+7EMAzat29P3759admyZXFlFBHxusoRNeEkpFlzzY4iIiIiIlLsilT8A7Rs2VKFvoiUetUqJcB+SLGBM9eJzc9mdiQRERERkWKj4flF5LJ0RVxTALKtFvYe2WluGBERERGRYqbiX0QuS8HBdiKcLgB2HfjF5DQiIiIiIsVLxb+IXLYinHkfgYdO7DA5iYiIiIhI8VLxLyKXLbsrAIAjjn0mJxERERERKV5FLv5Pnz5NZmam+/m+fft49dVXWb58uVeDiYgUN7s1BIDUM0dMTiIiIiIiUryKXPz37NmT9957D4C0tDRatWrFSy+9RM+ePZk+fbrXA4qIFJdwv0gA0nJTTU4iIiIiIlK8ilz8b9iwgbZt2wKwcOFCYmJi2LdvH++99x6vvfaa1wOKiBSXqPKVAUjnlMlJRERERESKV5GL/8zMTEJDQwFYvnw5N998M1arlauuuop9+9RvVkRKj5jweAAclmyTk4iIiIiIFK8iF/+1a9dm0aJFHDhwgGXLltG5c2cAjh49SlhYmNcDiogUl7joegCk2FwmJxERERERKV5FLv7Hjh3LI488QvXq1WnZsiWtW7cG8u4CaNasmdcDiogUlzpxTQDIsFk5lnbU5DQiIiIiIsXHr6gvuPXWW7nmmms4fPgwTZo0cS/v2LEjN910k1fDiYgUp5ioOMq7XGRarezYv5GK4Z3NjiQiIiIiUiyKXPwDxMbGEhsby8GDBwGoWrUqLVu29GowEZFiZ7EQ5bSQaYX9yVu4urGKfxEREREpm4p827/L5WLChAnY7Xbi4+OJj48nPDycZ599FpdL/WZFpHSxu8oBkOzYa24QEREREZFiVOQr/08++SRvv/02L774Im3atAHgm2++Ydy4cZw5c4bnn3/e6yFFRIqLnWDAQUrmIbOjiIiIiIgUmyIX/++++y5vvfUWPXr0cC9r3LgxVapU4b777lPxLyKlit0vHHCQmn3c7CgiIiIiIsWmyLf9p6SkUK9evQLL69WrR0pKildCiYiUlIiAaADSXSdNTiIiIiIiUnyKXPw3adKEadOmFVg+bdo0j9H/RURKg+iwOAAc1jMmJxERERERKT5Fvu1/0qRJ3HDDDXz11Ve0bt0agHXr1nHgwAG++OILrwcUESlOVaLqQAqkWZ1mRxERERERKTZFvvLfvn17tm/fzk033URaWhppaWncfPPNbNu2jbZt2xZHRhGRYlOrSkMAUmwWMrNOmZxGRERERKR4FOnKf05ODl26dGHGjBka2E9EyoTqlRPwMwxyLRZ2HNhEk9qtzI4kIiIiIuJ1RbryX65cOX799dfiyiIiUuL8yvkT6TQA2Htos8lpRERERESKR5Fv+7/jjjt4++23iyOLiIgpIpx5N0EdOrHL5CQiIiIiIsWjyAP+5ebm8s477/DVV1/RvHlzgoODPda//PLLXgsnIlIS7EYgkMmJjINmRxERERERKRZFLv43bdrEP/7xDwC2b9/usc5isXgnlYhICbLbwoBMUrOOmh1FRERERKRYFLn4X7VqVXHkEBExTbh/RTCScTgdZkcRERERESkWRe7z73A4SElJKbA8JSWF9PR0r4QSESlJFUIqA+CwZJqcRERERESkeBS5+O/duzfz588vsPzDDz+kd+/eXgklIlKSKoXXBCDNmmtyEhERERGR4lHk4n/9+vVce+21BZZ36NCB9evXeyWUiEhJio9NACDFBi6n0+Q0IiIiIiLeV+TiPysri9zcglfHcnJyOH36tFdCiYiUpCuqNQEg22ph3xFN9yciIiIiZU+Ri/+WLVsyc+bMAstnzJhB8+bNvRJKRKQkhYREEO50AbDz4K8mpxERERER8b4ij/b/3HPP0alTJ3755Rc6duwIwIoVK/jhhx9Yvny51wOKiJSECKeVNBv8cWz7hRuLiIiIiJQyRb7y36ZNG9atW0dcXBwffvghn332GbVr1+bXX3+lbdu2xZFRRKTYhbsCADiavs/kJCIiIiIi3lfkK/8ATZs2Zc6cOd7OIiJiGrs1BMgi5cwRs6OIiIiIiHhdka/8f/HFFyxbtqzA8mXLlvHll196JZSISEkL94sEwJGTanISERERERHvK3LxP3r0aJxnmQrLMAxGjx7tlVAiIiUtsnwlABxGhslJRERERES8r8jF/44dO0hISCiwvF69euzcudMroURESlqMvRoADmu2yUlERERERLyvyMW/3W5n9+7dBZbv3LmT4OBgr4QSESlp1aLrApBic5mcRERERETE+4pc/Pfs2ZMHH3yQXbt2uZft3LmThx9+mB49eng1nIhISakd1xSADJuVE2nHzA0jIiIiIuJlRS7+J02aRHBwMPXq1aNGjRrUqFGD+vXrExUVxf/93/8VR0YRkWIXE1WN8q68q/7bDmw0N4yIiIiIiJcVeao/u93O2rVrSUpK4pdffiEoKIjGjRvTrl274sgnIlIiLFYrkU4LmVY4kLwVGl1vdiQREREREa8pcvEPYLFY6Ny5M507d/Z2HhER04S7ynGQXJLTCo5rIiIiIiJSml1U8X/q1Cm+/vpr9u/fT3a258jYI0eO9EowEZGSZqc8kM6JzENmRxERERER8aoiF/8///wz3bp1IzMzk1OnThEZGcnx48cpX7480dHRKv5FpNSy28KBdNKyT5gdRURERETEq4o84N9DDz1E9+7dSU1NJSgoiO+++459+/bRvHlzDfgnIqVaRGA0AOmudJOTiIiIiIh4V5GL/40bN/Lwww9jtVqx2WxkZWURFxfHpEmTeOKJJ4ojo4hIiYgOjQMgzZJlchIREREREe8qcvFfrlw5rNa8l0VHR7N//34gbxaAAwcOeDediEgJqhJVG4A0W67JSUREREREvKvIff6bNWvGDz/8QJ06dWjfvj1jx47l+PHjvP/++zRs2LA4MoqIlIgalRvBTki1WTiddZqggCCzI4mIiIiIeEWRr/y/8MILVKpUCYDnn3+eiIgI7r33Xo4dO8bMmTO9HlBEpKTUrNoAP8PAZbGw48Ams+OIiIiIiHhNka/8t2jRwv1zdHQ0S5cu9WogERGz+JXzJ9JpcNTPwt5Dm2lc+0qzI4mIiIiIeEWRr/yLiJRlEc6870QPpew0OYmIiIiIiPeo+BcR+YswAgE4nnHQ5CQiIiIiIt6j4l9E5C/CrWEApGYdNTmJiIiIiIj3qPgXEfmLcP8KADicaeYGERERERHxIhX/IiJ/ERVcGYB0TpucRERERETEewpd/Hfr1g2Hw+F+/uKLL5KWluZ+fuLECRISEoq08zVr1tC9e3cqV66MxWJh0aJF7nU5OTk8/vjjNGrUiODgYCpXrkz//v05dOiQxzaqV6+OxWLxeLz44osebX799Vfatm1LYGAgcXFxTJo0qUCWBQsWUK9ePQIDA2nUqBFffPFFkY5FRMqGyuE1AUiz5picRERERETEewpd/C9btoysrCz38xdeeIGUlBT389zcXLZt21aknZ86dYomTZrw+uuvF1iXmZnJhg0bePrpp9mwYQMff/wx27Zto0ePHgXaTpgwgcOHD7sf999/v3tdeno6nTt3Jj4+np9++onJkyczbtw4Zs6c6W6zdu1a+vTpw5AhQ/j555/p1asXvXr1YtMmzfMtcrmJj60PQIoNXE6nyWlERERERLzDr7ANDcM47/OL0bVrV7p27XrWdXa7naSkJI9l06ZNo2XLluzfv59q1aq5l4eGhhIbG3vW7cyZM4fs7Gzeeecd/P39adCgARs3buTll19m2LBhAEyZMoUuXbrw6KOPAvDss8+SlJTEtGnTmDFjxiUfp4iUHnXim8KPkGW1sP/oHqpXqm12JBERERGRS1aq+vw7HA4sFgvh4eEey1988UWioqJo1qwZkydPJjc3171u3bp1tGvXDn9/f/eyxMREtm3bRmpqqrtNp06dPLaZmJjIunXriu9gRMQnhYZEYne6ANh54BeT04iIiIiIeEehr/zn96f/+7KScubMGR5//HH69OlDWFiYe/nIkSP5xz/+QWRkJGvXrmXMmDEcPnyYl19+GYDk5GRq1Kjhsa2YmBj3uoiICJKTk93L/tomOTn5nHmysrI8ukGkp6df8jGKiG+IdFpx2OCP49vNjiIiIiIi4hVFuu1/4MCBBAQEAHnF+D333ENwcDCARyHsbTk5Odx+++0YhsH06dM91o0aNcr9c+PGjfH39+fuu+9m4sSJ7qzFYeLEiYwfP77Yti8i5gl3+QPZHHHsNzuKiIiIiIhXFLr4HzBggMfzO+64o0Cb/v37X3qiv8kv/Pft28fKlSs9rvqfTatWrcjNzWXv3r3UrVuX2NhYjhw54tEm/3n+OAHnanOucQQAxowZ4/HFQ3p6OnFxcUU6NhHxTWGWECCF1DOHzY4iIiIiIuIVhS7+Z82aVZw5ziq/8N+xYwerVq0iKirqgq/ZuHEjVquV6OhoAFq3bs2TTz5JTk4O5cqVAyApKYm6desSERHhbrNixQoefPBB93aSkpJo3br1OfcTEBBQrHcWiIh5wstFAimk5aSaHUVERERExCsKXfyfy759+zh16hT16tXDai3a+IEZGRns3LnT/XzPnj1s3LiRyMhIKlWqxK233sqGDRtYsmQJTqfT3Qc/MjISf39/1q1bx/r167n22msJDQ1l3bp1PPTQQ9xxxx3uwr5v376MHz+eIUOG8Pjjj7Np0yamTJnCK6+84t7vAw88QPv27XnppZe44YYbmD9/Pj/++KPHdIAicvmILB8LZ3aSbpwyO4qIiIiIiFcUulp/55133IPo5Rs2bBg1a9akUaNGNGzYkAMHDhRp5z/++CPNmjWjWbNmQF7//WbNmjF27Fj++OMPPv30Uw4ePEjTpk2pVKmS+7F27Vog7+r7/Pnzad++PQ0aNOD555/noYce8ija7XY7y5cvZ8+ePTRv3pyHH36YsWPHuqf5A7j66quZO3cuM2fOpEmTJixcuJBFixbRsGHDIh2PiJQNMWHxADisxTeWiYiIiIhISbIYhmEUpuFVV13F3XffzaBBgwBYunQp3bt3Z/bs2dSvX58RI0aQkJDAW2+9VayBfVV6ejp2ux2Hw3HBcQlExLf996ePuW/TM4Q6XawdvNnsOCIiIiIiZ1WUOrTQV/537NhBixYt3M8XL15Mz5496devH//4xz944YUXWLFixcWnFhHxEbXimgJw0mYlJf24uWFERERERLyg0MX/6dOnPb5JWLt2Le3atXM/r1mzprtPvohIaVapQnWCXC4Atu3baG4YEREREREvKHTxHx8fz08//QTA8ePH2bx5M23atHGvT05Oxm63ez+hiEgJs1itRDotAOw/stXkNCIiIiIil67Qo/0PGDCA4cOHs3nzZlauXEm9evVo3ry5e/3atWs1QJ6IlBnhrnL8QS5HUnebHUVERERE5JIVuvh/7LHHyMzM5OOPPyY2NpYFCxZ4rP/222/p06eP1wOKiJjBThBwkuOZh8yOIiIiIiJyyQpd/FutViZMmMCECRPOuv7vXwaIiJRmdls4cBJHtgb8ExEREZHSr9B9/kVELieRgdEAOFwnTU4iIiIiInLpCn3lv2bNmoVqt3u3+seKSOlXMTQOUn/CYTljdhQRERERkUtW6OJ/7969xMfH07dvX6Kjo4szk4iI6SpH1oZUSLPlmh1FREREROSSFbr4/+CDD3jnnXd4+eWX6dq1K4MHD6Zbt25Yreo5ICJlT60qDWEXpNgsnM46TVBAkNmRREREREQuWqEr99tuu40vv/ySnTt30rx5cx566CHi4uIYPXo0O3bsKM6MIiIlrkaVBtgMA5fFwq6Dv5sdR0RERETkkhT5sn2VKlV48skn2bFjB3PnzmX9+vXUq1eP1NTU4sgnImKKcv6BRDkNAPYc3mRyGhERERGRS1Po2/7/6syZMyxcuJB33nmH9evXc9ttt1G+fHlvZxMRMVW408ZRP4M/Tuw0O4qIiIiIyCUpUvG/fv163n77bT788ENq1qzJ4MGD+eijj4iIiCiufCIiprEbQUAmJ04eMDuKiIiIiMglKXTx36BBA44ePUrfvn35+uuvadKkSXHmEhExnd0WCmSSknXM7CgiIiIiIpek0MX/li1bCA4O5r333uP9998/Z7uUlBSvBBMRMVuEfwUwjuBwppkdRURERETkkhS6+J81a1Zx5hAR8TlR5SvDqc2kk2l2FBERERGRS1Lo4n/AgAHFmUNExOdUiqgBpyDNmmN2FBERERGRS1Lkqf5ERC4X8bEJAKTYwOV0mpxGREREROTiqfgXETmHOvF5A5tmWS0cOLbP5DQiIiIiIhdPxb+IyDmEhVTA7nQBsPPALyanERERERG5eCr+RUTOI9KZ9zF58Ng2k5OIiIiIiFw8Ff8iIudhd/kDcDRdt/2LiIiISOlV6NH+8zmdTmbPns2KFSs4evQoLpfLY/3KlSu9Fk5ExGxh1hAghZTTyWZHERERERG5aEUu/h944AFmz57NDTfcQMOGDbFYLMWRS0TEJ0T4RQApOHJSzY4iIiIiInLRilz8z58/nw8//JBu3boVRx4REZ8SGRQLWbtwGBlmRxERERERuWhF7vPv7+9P7dq1iyOLiIjPiQ6LB8BhzTI5iYiIiIjIxSty8f/www8zZcoUDMMojjwiIj4lLrouAKk21wVaioiIiIj4riLf9v/NN9+watUqvvzySxo0aEC5cuU81n/88cdeCyciYrbacY1hM6TbrKSeTCEiNNLsSCIiIiIiRVbk4j88PJybbrqpOLKIiPicShVqEOhyccZqZfv+X2jV4FqzI4mIiIiIFFmRi/9Zs2YVRw4REZ9ktdmIclr4wwr7kn9X8S8iIiIipVKR+/yLiFxuwl1535Mmp+42OYmIiIiIyMUp8pV/gIULF/Lhhx+yf/9+srOzPdZt2LDBK8FERHyFnfLASY5nHjI7ioiIiIjIRSnylf/XXnuNQYMGERMTw88//0zLli2Jiopi9+7ddO3atTgyioiYym6zA+DIOm5yEhERERGRi1Pk4v+NN95g5syZTJ06FX9/fx577DGSkpIYOXIkDoejODKKiJgqMjAaAIcr3eQkIiIiIiIXp8jF//79+7n66qsBCAoK4uTJkwDceeedzJs3z7vpRER8QIWQqgCkW86YnERERERE5OIUufiPjY0lJSUFgGrVqvHdd98BsGfPHgzD8G46EREfUDmqNgCptlyTk4iIiIiIXJwiF//XXXcdn376KQCDBg3ioYce4vrrr+ef//wnN910k9cDioiYrWblhgCk2ixkZWeZnEZEREREpOiKPNr/zJkzcblcAAwfPpyoqCjWrl1Ljx49uPvuu70eUETEbLWqNMRmGDgtFnYe/J0GNZuZHUlEREREpEiKXPxbrVas1v/dMNC7d2969+7t1VAiIr6kXEAQkU6DY34Wdh/6TcW/iIiIiJQ6Rb7tH+C///0vd9xxB61bt+aPP/4A4P333+ebb77xajgREV8R4bQBcPjETpOTiIiIiIgUXZGL/48++ojExESCgoL4+eefycrK6//qcDh44YUXvB5QRMQXhBmBABzLOGByEhERERGRoity8f/cc88xY8YM3nzzTcqVK+de3qZNGzZs2ODVcCIivsJuDQUg9cxRk5OIiIiIiBRdkYv/bdu20a5duwLL7XY7aWlp3sgkIuJzIvyjAHA408wNIiIiIiJyEYpc/MfGxrJzZ8E+r9988w01a9b0SigREV9TIbgKAOlkmpxERERERKToilz833XXXTzwwAOsX78ei8XCoUOHmDNnDo888gj33ntvcWQUETFdbHgNANKsOSYnEREREREpuiJP9Td69GhcLhcdO3YkMzOTdu3aERAQwCOPPML9999fHBlFRExXLaY+/AEpNnC5XB5TnoqIiIiI+DqLYRjGxbwwOzubnTt3kpGRQUJCAiEhId7OVqqkp6djt9txOByEhYWZHUdEvCwt/RhtP7kOgM8TP6NabHVzA4mIiIjIZa8odWiRr/zn8/f3JyEh4WJfLiJSqoSHVSTM6SLdZmXnwV9U/IuIiIhIqVLo4n/w4MGFavfOO+9cdBgREV8W6bSSboODx7YCPc2OIyIiIiJSaIUu/mfPnk18fDzNmjXjInsKiIiUauEufyCbI459ZkcRERERESmSQhf/9957L/PmzWPPnj0MGjSIO+64g8jIyOLMJiLiU8IswUA2qaePmB1FRERERKRICj1c9euvv87hw4d57LHH+Oyzz4iLi+P2229n2bJluhNARC4L4X55X3im5Z4wOYmIiIiISNEUaa6qgIAA+vTpQ1JSEr///jsNGjTgvvvuo3r16mRkZBRXRhERnxAZFANAukufdyIiIiJSulz0RNVWqxWLxYJhGDidTm9mEhHxSTH2eADSrFkmJxERERERKZoiFf9ZWVnMmzeP66+/niuuuILffvuNadOmsX//fkJCQooro4iIT4ireAUAqTaXyUlERERERIqm0AP+3XfffcyfP5+4uDgGDx7MvHnzqFChQnFmExHxKbWqNoLfId1mJS0jjfCQcLMjiYiIiIgUisUo5Gh9VquVatWq0axZMywWyznbffzxx14LV5qkp6djt9txOByEhYWZHUdEioHL6aTle43Jslp5u8VUWjboYHYkEREREbmMFaUOLfSV//79+5+36BcRKeusNhtRTjhkhX3Jv6v4FxEREZFSo9DF/+zZs4sxhohI6RDhKschnBxO2212FBERERGRQrvo0f5FRC5HYQQBcOLUIZOTiIiIiIgUnop/EZEisNvsAKRlHzc5iYiIiIhI4an4FxEpgsiAaADSnekmJxERERERKTwV/yIiRVAhpCoADstpk5OIiIiIiBSeqcX/mjVr6N69O5UrV8ZisbBo0SKP9YZhMHbsWCpVqkRQUBCdOnVix44dHm1SUlLo168fYWFhhIeHM2TIEDIyMjza/Prrr7Rt25bAwEDi4uKYNGlSgSwLFiygXr16BAYG0qhRI7744guvH6+IlH5VomoBkGrLNTmJiIiIiEjhmVr8nzp1iiZNmvD666+fdf2kSZN47bXXmDFjBuvXryc4OJjExETOnDnjbtOvXz82b95MUlISS5YsYc2aNQwbNsy9Pj09nc6dOxMfH89PP/3E5MmTGTduHDNnznS3Wbt2LX369GHIkCH8/PPP9OrVi169erFp06biO3gRKZVqVGoIQKrNQlZ2lslpREREREQKx2IYhmF2CACLxcInn3xCr169gLyr/pUrV+bhhx/mkUceAcDhcBATE8Ps2bPp3bs3W7ZsISEhgR9++IEWLVoAsHTpUrp168bBgwepXLky06dP58knnyQ5ORl/f38ARo8ezaJFi9i6dSsA//znPzl16hRLlixx57nqqqto2rQpM2bMKFT+9PR07HY7DoeDsLAwb50WEfExWVmZtJzXEpfFwoft/0P96k3MjiQiIiIil6mi1KE+2+d/z549JCcn06lTJ/cyu91Oq1atWLduHQDr1q0jPDzcXfgDdOrUCavVyvr1691t2rVr5y78ARITE9m2bRupqanuNn/dT36b/P2IiOQLCChPpDPvO9NdB381OY2IiIiISOH4bPGfnJwMQExMjMfymJgY97rk5GSio6M91vv5+REZGenR5mzb+Os+ztUmf/3ZZGVlkZ6e7vEQkctDhNMGwKGUnSYnEREREREpHJ8t/n3dxIkTsdvt7kdcXJzZkUSkhIQbgQAcO3nA5CQiIiIiIoXjs8V/bGwsAEeOHPFYfuTIEfe62NhYjh496rE+NzeXlJQUjzZn28Zf93GuNvnrz2bMmDE4HA7348ABFQEil4swaygAqWeOXqCliIiIiIhv8Nniv0aNGsTGxrJixQr3svT0dNavX0/r1q0BaN26NWlpafz000/uNitXrsTlctGqVSt3mzVr1pCTk+Nuk5SURN26dYmIiHC3+et+8tvk7+dsAgICCAsL83iIyOUh3D8KgHRnmrlBREREREQKydTiPyMjg40bN7Jx40Ygb5C/jRs3sn//fiwWCw8++CDPPfccn376Kb/99hv9+/encuXK7hkB6tevT5cuXbjrrrv4/vvv+fbbbxkxYgS9e/emcuXKAPTt2xd/f3+GDBnC5s2b+eCDD5gyZQqjRo1y53jggQdYunQpL730Elu3bmXcuHH8+OOPjBgxoqRPiYiUAhXK532+ODhlchIRERERkcLxM3PnP/74I9dee637eX5BPmDAAGbP/v/27jM8iqoPw/g9u+mdQEjovXeUDoICBsSCFUURXyuKAmIBRUVABQsWVMAKFopYEBDpvYNEmvTeQgIBUkjfnffDQiAQIECSScLzu66Q7MyZ2Wd2k5D/zJlzxvLqq69y6tQpnn76aU6ePEnLli2ZOXMmXl5eGduMGzeO559/nrZt22Kz2bj33nsZMWJExvrAwEBmz55Nz549ueGGGyhWrBhvvfUWTz/9dEab5s2bM378eN544w1ef/11qlSpwp9//knt2rXz4FUQkYImLKg8JEKsLe2ybUVERERE8gPDNE3T6hCFwZXMrygiBduaDbN4/N+X8XKarOq+AZst395BJSIiIiKF2JXUofqLVUTkClUpVx+AZJvBoWMa7FNERERE8j8V/yIiVygoMBR/hxOAXQfWW5xGREREROTyVPyLiFyFog7Xr8/9R7danERERERE5PJU/IuIXIVApzsA0bH7LE4iIiIiInJ5Kv5FRK5CgOEHQEziEYuTiIiIiIhcnop/EZGrEOReBIDYtOMWJxERERERuTwV/yIiV6GoVygAcWa8xUlERERERC5Pxb+IyFUIDSgLQKwtxeIkIiIiIiKXp+JfROQqlAqpBsAJu9PiJCIiIiIil6fiX0TkKlQuXQeAWLuN2FMnrQ0jIiIiInIZKv5FRK5CqdDKeDpNALbt3WBxGhERERGRS1PxLyJyFWx2N4o6XMX//qjNFqcREREREbk0N6sDiIgUVEFOdw7jIPLELqujiIiIiOQZh9NBRHQERxOPEuITQsPiDbHb7FbHkstQ8S8icpUC8QYSiDl1yOooIiIiInli7r65DFs9jKjEqIxloT6h9G/cn3bl2lmYTC5H3f5FRK5SoD0QgBOpxyxOIiIiIpL75u6bS9+FfTMV/gDRidH0XdiXufvmWpRMskPFv4jIVSriGQJAnDPO4iQiIiIiucvhdDBs9TBMzAvWnVn2/ur3cTgdeR1NsknFv4jIVQrxLQ1AHEkWJxERERHJXRHRERdc8T+XicmRxCNEREfkYSq5Eir+RUSuUsmilQA4YdcZbhERESncjiYezdF2kvdU/IuIXKUKJWsBcNwOKampFqcRERERyT0hPiE52k7ynop/EZGrVKlMXWymicMw2HV4q9VxRERERHJNnWJ18LB5XHS9gUGYTxgNizfMw1RyJVT8i4hcJU9PX4IdrgFu9hzaaHEaERERkdxhmibDVg8j1Xnpno79GvfDbrPnUSq5Uir+RUSuQRGH6z+4wzE7LU4iIiIikjt++O8Hft/xOzbDxhO1nyDUJzTTen8Pfz5u8zHtyrWzKKFkh4p/EZFrEGh6AhAdv9/iJCIiIiI5b96+eXy89mMAXrnxFfrc0IdZ987i+/Dvubvy3QAEeQRxS9lbrIwp2aDiX0TkGgTa/AE4mXzxqW9ERERECqJNxzbRf0l/TEwerPYgD9d4GAC7zU6jsEb0b9wff3d/DiQcYNmhZRanlctR8S8icg2CPIoCEOs4aW0QERERkRwUmRDJC/NfINmRTMtSLenXuB+GYWRq4+Puw12V7wJg/NbxVsSUK6DiX0TkGhT1KQFAHIkWJxERERHJGQmpCTw37zmOJR2japGqfNT6I9xsblm2faj6QxgYLD20lH1x+/I4qVwJFf8iItcgNKgCACdtlx79VkRERKQgSHem8/Lil9l5cich3iF82fZLfN19L9q+bEBZWpZqCcDErRPzKqZcBRX/IiLXoHxoDQCO28HpdFqcRkREROTqnZnSb9mhZXi7efN5288J8w277HZda3QF4M+df5KYpt6Q+ZWKfxGRa1C1bH0AkmwGkTGHrA0jIiIicg1+2vwTv2z7BQODoa2GUqtorWxt17xkc8oFlCMhLYFpu6blckq5Wir+RUSuQVBQGP4O1xX/HQfWWRtGRERE5Cot2L+Aj/75CICXbnyJtmXbZntbm2HjwWoPAjBh6wRM08yVjHJtVPyLiFyjYIdr5NsD0dssTiIiIiJy5TbHbKbfkn6YmNxf9X4erfnoFe/jrsp34e3mza7YXaw+sjoXUsq1UvEvInKNAp0eAETH7rU2iIiIiMgVOnLqCC/Me4Gk9CSal2zOa01eu2BKv+zw9/Dnzkp3AjB+i6b9y49U/IuIXKMgwzUCbkxipMVJRERERLLvVNopnp/3PNFJ0VQOqsxHrT/C3eZ+1ft7qPpDACw8uJDDCYdzKqbkEBX/IiLXKNC9CACx6cctTiIiIiKSPenOdF5d/CrbTmyjqFdRvmz7Jf4e/te0z0pBlWhSoglO08nEbZr2L79R8S8ico2CvUIBiHUmWJxEREREJHs+XPMhiw8uxsvuxee3fE5Jv5I5st+u1V3T/v2x4w+S05NzZJ+SM1T8i4hco+L+ZQGIs6VYnERERETk8sZtGcf4ra778t9r9R51Qurk2L5bl25NSd+SxKbEMmPPjBzbr1w7Ff8iIteodEhVAE7YHRYnEREREbm0xQcX88GaDwDo07AP7cu1z9H92212ulTvAsD4reM17V8+ouJfROQaVSldF4CTdhtxp+IsTiMiIiKStW3Ht/Hyopdxmk7uqXIPj9d+PFee557K9+Bp92Tr8a2sO7ouV55DrpyKfxGRa1QqrAoeTtdZ7e3711kbRkRERCQL0YnR9JzXk6T0JJqUaMIbTd+4qin9siPIK4hOFTsBmvYvP1HxLyJyjWx2N4o6XMX/3iNbLE4jIiIiklliWiLPz3ueqMQoKgZW5OM2H1/TlH7ZcWbav7n75hKdGJ2rzyXZo+JfRCQHFHG6ARB5YrfFSURERETOcjgd9FvSjy3HtxDsFcyXbb8kwCMg15+3enB1GhZvSLqZzqRtk3L9+eTyVPyLiOSAANMHgOMJBy1OIiIiInLW8LXDWXhgIR42Dz67+TNK+5fOs+d+qIbr6v+v238l1ZGaZ88rWVPxLyKSA4LsgQAcTz1mcRIRERERl1+2/sJPm38C4N1W71K/eP08ff62ZdtS3Ls4x5OPM3vf7Dx9brmQin8RkRxQxLMYAHGOWIuTiIiIiMDSQ0sZunooAL0a9KJD+Q55nsHd5s791e4HYMKWCXn+/JKZin8RkRxQzM/VhS7OSLI4iYiIiFzvtp/YzsuLXsZhOriz0p08WedJy7LcV/U+3G3ubDi2gU3HNlmWQ1T8i4jkiJLBlQA4aXdYnERERESuZ8eSjtFzXk9OpZ2iUVgj3m72dq5N6ZcdxbyLEV4+HIAJW3X130oq/kVEckCFErUAiLFDWnq6xWlERETkepSUnsQL817gyKkjlA8ozydtPsHdnrtT+mXHmWn/ZuyZQUxSjMVprl8q/kVEckClcvUwTBOHYbDr8Bar44iIiMh1xmk6eX3J62yK2USQZxAj244k0DPQ6lgA1A2pS+2itUlzpvH7jt+tjnPdUvEvIpIDvDx9CXaYAOw+uNHiNCIiInK9+XTtp8zdPxd3mzuf3fwZZQLKWB0pk641ugLwy7ZfSHeql6QVVPyLiOSQIk47AIdjdlqcRERERK4nv23/jTH/jQFgSIshNAxtaHGiC4WXDyfYK5joxGjm759vdZzrkop/EZEcEuT0BCA6fr/FSUREROR6sfzwct5Z+Q4Az9V/jk4VO1mcKGsedg/urXIvAOO3jrc4zfVJxb+ISA4JsPkDcCI5yuIkIiIicj3YeWInLy18CYfp4PaKt9Ojbg+rI13SA9UewG7YWRu1lm3Ht1kd57qj4l9EJIcEeRQFIDb9pLVBREREpNA7M6VfQloCDYs3ZFDzQZZO6ZcdYb5htC3bFtC0f1ZQ8S8ikkOKeYcBEMcpi5OIiIiIZZwO2LMENv7m+ux05PhTJKcn03t+bw6fOkxZ/7J8dvNneNg9cvx5csOZaf+m755ObEqsxWmuLyr+RURySFhQBQBibWkWJxERERFLbJ4Kn9aGH26H359wff60tmt5DnGaTgYsHcCGYxsI9Azky7ZfEuQVlGP7z203hN5A1SJVSXYkM3nHZKvjXFdU/IuI5JCyodUBiLGbmKZpcRoRERHJU5unwqRHIe5w5uVxka7lOXQC4PN/P2f2vtm42dz4tM2nlA8snyP7zSuGYdC1umvav4nbJuLIhZ4RkjUV/yIiOaRq2foAJNkMIo8dvnRjERERKTycDpjZD8jq5P/pZTP7X/MtAJN3TObbjd8CMKj5IG4Mu/Ga9meV2yreRoBHAIcSDrHk0BKr41w3VPyLiOSQIkEl8HM4Adh+YL3FaURERCTP7Ft+4RX/TEyIO+Rqd5VWRa5i8IrBADxT9xnurHTnVe/Lat5u3txT5R4Axm/RtH95RcW/iEhOMQyKOlyj7B48utXiMCIiIpJnErI5zW92251nd+xuXlz4IulmOh0rdKRn/Z5XtZ/8pEu1LhgYrIhcwe7Y3VbHuS6o+BcRyUGBpmuk3aiTeyxOIiIiInki8Ths/jN7bb2Dr3j3x5OP89zc54hPjad+SH2GtBiS76f0y47S/qVpXaY1ABO3TrQ4zfVBxb+ISA4KxBeA44lHLE4iIiIiuSo9FVZ8CSPqw5Zp2dtmRr8r6vqf4kih9/zeHEo4RGm/0nx2y2d42j2vLm8+dGbgvyk7p5CQmmBxmsJPxb+ISA4KcgsCIDY9xtogIiIikjtM0zVy/5eNYdbrkBwLobWhdT/AOP1xrtOPPQMgZjuM6QhTX3D1GLjk05i8uexN1h1dh7+HP1+2+5JgryvvOZCfNS3RlAqBFUhMT2TKrilWxyn0VPyLiOSgIl6hAJx0xlucRERERHLcoQgYcxtM6gYn9oBfKNz5OTyzGG5+HR74EQJKZN4moCQ88BP02QANu7uWRfzoOnmw4VfXyYQsfLnuS2bsmYGb4cYnbT6hYmDFXD64vGcYBg9Vfwhwdf13mk6LExVublYHEBEpTEIDykLMKuJsKVZHERERkZwSewjmDYYNp+9Nd/OG5i9Ai97g6Xe2Xc07oXonV9f+hCjXyYFyzcFmd62/cwTUewj+6gNHt8IfT8L68dBpOASfLe6n7prKVxu+AuCtZm/RpESTPDrQvHdnpTv5LOIz9sbtZeXhlTQv1dzqSIWWrvyLiOSgUiFVADhpv7Z5fEVERCQfSEmA+e/C5zecLfzrPggv/AO3DMhc+J9hs0OFVlDnPtfnM4X/GeWawTNL4OY3wO4Ju+bDyGaw5GNwpLHmyBoGLh8IwJN1nuTuKnfn8kFay9fdl7sq3QXA+K2a9i835fviv3z58hiGccFHz56u6S3atGlzwboePXpk2sf+/fvp1KkTPj4+FC9enFdeeYX09PRMbRYuXEjDhg3x9PSkcuXKjB07Nq8OUUQKkcql6gBwwm4j7lScxWlERETkqjgdEPETfN4QFn8A6UlQtjk8tQDu+QoCS1/b/t08oPUr8OxyqHATpCfDvEHs/bolfeY9T7oznVvL3coLDV7ImePJ5850/V98cDEH4g9YnKbwyvfF/5o1a4iMjMz4mDNnDgD3339/RpunnnoqU5sPPvggY53D4aBTp06kpqayfPlyfvjhB8aOHctbb72V0WbPnj106tSJm2++mXXr1tGnTx+efPJJZs2alXcHKiKFQpkS1fFwuu7d23Fgg8VpRERE8h+H02TFrhimrDvEil0xOJxZ3/Numd0L4aubYOrzrq77RSq47tn/399QqmHOPlexyvDoVOg8mhM+wfR0jyUuPZG6boG8e8Or2Ix8X67liPKB5WlRsgUmJr9s/cXqOIVWvr/nPyQkJNPjYcOGUalSJVq3bp2xzMfHh7CwsCy3nz17Nps3b2bu3LmEhoZSv359hgwZQr9+/Xj77bfx8PBg9OjRVKhQgeHDhwNQo0YNli5dyieffEJ4eHjuHZyIFDo2uxvBDpMjNoO9kZu5oXpLqyOJiIjkGzM3RTJo2mYiY5MzlpUI9GLgHTXpULvEJbbMA0e3w5w3YftM12OvQLjpVWj8FLjl4vR6hkFqnXvpc3Aa+2M2Uiotnc/2/YfXV62g4/tQszMY588gUPh0rdGVZYeX8cfOP3iu/nP4uPtYHanQKVCnklJTU/n55595/PHHMc75ARg3bhzFihWjdu3avPbaayQmJmasW7FiBXXq1CE0NDRjWXh4OHFxcfz3338Zbdq1a5fpucLDw1mxYsVFs6SkpBAXF5fpQ0QEoIjTdV418sRui5OIiIjkHzM3RfLszxGZCn+AI7HJPPtzBDM3RVoT7FQM/P0KjGzqKvxtbtCkB/RaB82fz93CH9eUfgOXDyQiZiN+7n582fRtigVVcPU6+PUxGN8FTu7P1Qz5QYuSLSjtV5r41Him75ludZxCqUAV/3/++ScnT57ksccey1jWtWtXfv75ZxYsWMBrr73GTz/9xCOPPJKx/siRI5kKfyDj8ZEjRy7ZJi4ujqSkpCyzDB06lMDAwIyPMmXK5MQhikghEIg3ADEJBy1OIiIikj84nCaDpm0mqw7+Z5YNmrY5b28BSE+B5Z/DiAaw+mswHVDtNnhupeuKu09wnsQYvWE0f+3+C7thZ3ib4VSq3cU1FkDrfmBzhx2z4MsmrqyO9MvvsICy2+w8WP1BAMZvGY95kSkQ5eoVqOL/u+++o2PHjpQsWTJj2dNPP014eDh16tTh4Ycf5scff2Ty5Mns2rUrV7O89tprxMbGZnwcOKCBKUTEJdAWCMCJ1GMWJxEREckfVu85fsEV/3OZQGRsMqv3HM/9MKYJ//0JXzaG2W9ASiyE1XHde//QBChWJfcznDZ993RGrhsJwBtN36B5ydPT3Ll7wc2vw7PLXAMNpiW6sn5zMxyKyLN8ea1z5c54u3mz8+RO/on6x+o4hU6BKf737dvH3LlzefLJJy/ZrkkT1xyYO3fuBCAsLIyoqKhMbc48PjNOwMXaBAQE4O3tneXzeHp6EhAQkOlDRASgiGcxAOIcsRYnERERyR+i4y9e+J/rSGzWvW5zzMG18H0H+LU7nNgLfmFw15fw9CKo2Pqym+ekiKgI3lz2JgD/q/U/7qt634WNQqrBY9Phzs/BKwiObIBv28KM/pASn6d580KgZyCdKnYCYMLWCRanKXwKTPE/ZswYihcvTqdOnS7Zbt26dQCUKOEaMKRZs2Zs3LiR6OjojDZz5swhICCAmjVrZrSZN29epv3MmTOHZs2a5eARiMj1opifa/qfWCOX/4AREREpIIr7e2Wr3bCZWxm3ah/JaY6cDXDyAPz+FHx7CxxYCW7erm71L6yFBo+AzZ6zz3cZ++P203tBb9KcabQr244+N/S5eGObDRo+Cs//A3UeANMJq0a5bgXYWvjujT8z7d/8/fM5cuqIxWkKlwJR/DudTsaMGUP37t1xczs7QcGuXbsYMmQIa9euZe/evUydOpVHH32Um266ibp16wJw6623UrNmTbp168b69euZNWsWb7zxBj179sTT0zV4R48ePdi9ezevvvoqW7duZeTIkUyaNIkXX3zRkuMVkYKtZJGKAJy0Fd778kRERK5E4wrBhAZceuA8w4CouBQGTN5Eqw8W8NWiXcQnp13bE6fEw7zB8MWNsHGSa1m9rq6i/+bXwdPv2vZ/FWJTYuk5rycnU05Su2ht3mv1Xvam9PMLgXu/gUf+gCLlIe4QTOwKEx+G2EO5njuvVC1SlUZhjXCYDn7Zpmn/clKBKP7nzp3L/v37efzxxzMt9/DwYO7cudx6661Ur16dl156iXvvvZdp06ZltLHb7fz111/Y7XaaNWvGI488wqOPPsrgwYMz2lSoUIHp06czZ84c6tWrx/Dhw/n22281zZ+IXJUKJVy9io67QVq6TgCIiIjYbQa1SwZmuc44/fFpl/q8dXtNSgR6cTQ+haEzttJi2Hw+mrWNYwkpV/aETgesHQsjGsKS4ZCeDOVawtML4e5REFjqGo/o6qQ50nhx4YvsjdtLCd8SfN72c7zdsr7N+KIqt4VnV0DLF10zE2z9y9ULYNVXruMuBLpW7wrA79t/J8Vxhe+9XJRhahjFHBEXF0dgYCCxsbG6/1/kOpeUFEeTX5pjGga/3TyRamVrWR1JRETEUvtiTtHu40WkOUyCfTw4npiasa5EoBcD76hJh9qu23ZT051MWXeI0Yt2sevoKQA83Wx0aVSGp1pVpEzwZeZ/3zUfZr0B0a5pvQmuCO2HQPVOru4FFjFNkzeWvcHUXVPxdfflx44/UrVI1WvbadR/MK0PHFztelyyIdzxGZSoe815rZTuTKfjHx05cuoI77R4h7sq32V1pHzrSupQFf85RMW/iJyr9Xe1OO5m44OqA+jY7EGr44iIiFiq5/gIpm+I5KaqIYx5rBGr9xwnOj6Z4v5eNK4QjN12YVHudJrM3hzFqIU7WX/QNYiu3WZwZ72S9GhdiWph/pk3iN4Kc96EHbNdj72CXPf1N3oS3Dxy+Qgv75sN3zDi3xHYDTtftP2ClqVa5syOnU5Y+z3MHQQpcWDYodlz0OY18PDNmeewwLcbv+WziM+oWbQmEztNxLDwxE1+diV1aIHo9i8iUtAUcbp+vR46tsPiJCIiItaK2H+C6RsiMQx4rWN17DaDZpWKclf9UjSrVDTLwh/AZjPoUDuMP3u2YPyTTWhVpRgOp8nkfw8R/ulinvxhDWv3nYBTx2D6SzCquavwt7lBk2eh17+uIjgfFP4z98xkxL8jAHit8Ws5V/iDa0DARk/C82ugZmcwHbD8c/iyKWyfnXPPk8furXIvHjYPNsdsZsOxDVbHKRTcLt9ERESuVJDTE0jhaPx+q6OIiIhYxjRN3pu+BYD7GpamRokr7yFrGAbNKxejeeVibDh4ktGLdjFj0xGWbDlIpe3fUcNjCj5moqtx9duh3SAoVjknD+OKOJwOIqIjOJp4lBCfEOyGnQFLBwDQrWY3ulTvkjtP7B8GD/wA22fB9Jchdj+Mv991QqDj+671BUgRryJ0rNCRKbumMH7LeOqF1LM6UoGn4l9EJBcE2vyBFE4kR1kdRURExDKz/ovin30n8HK38dKt1a55f3VLBzGya0OOrJiAx4LBBKdFggmbnOX5KeBpWta4m9uCS5C3E/edNXffXIatHkZU4tn//w0MTEzalGnDSze8lPshqoZD+Zaw4D1YOQo2/wm7FkC7gXDD/1w9BQqIh2o8xJRdU5i9bzavJL1CMe9iVkcq0ArOOy8iUoAEuRcF4GT6CYuTiIiIWCPN4eT9mVsBeKpVRcICva59pwf/ge9uJWz2swSnReLwDWNq+TfowlB+OVaeFyb8yy3DFzJ+1X6S0/J25Pu5++bSd2HfTIU/gIlriLUO5Ttgt+XRaQkPXwh/F55eACUbQEosTO8L34dD1Oa8yZADahWtRb2QeqQ70/l1269WxynwVPyLiOSCoj6urnVxnLI4iYiIiDUmrN7PnmOnKObnwTOtK13bzk7uh9+egG/buka2d/eBNq9h7x3BnY+9wrL+7XixXVWK+LizLyaR1ydvpNUHC/hq0S7ik9Ny5oAuweF0MGz1sIxCPyufrv0UR15PxVeiHjw5Dzq8Dx5+rtfuq1auwQHTkvI2y1U6M+3fpO2TSHPk/ntZmKn4FxHJBaFBFQCItek/KRERuf7EJafx6VzXoLe921XFz/Mq7zZOjnMVqp/fCJt+Awyo/zC8sBba9M8YzT7Ix4Pe7aqwrP8tvHV7TUoEenE0PoWhM7bSYth8Ppq1jWMJuTdffER0xAVX/M93JPEIEdERuZbhomx2aNoDeq52jYngTIelH8PIpq5pEfO59uXaU8y7GMeSjjF3/1yr4xRoKv5FRHJBueLVAThud6IZVUVE5HozeuEujp9KpVKILw82KnPlO3Ckwz/fw+cNXYWqIwXKt4JnFkHnkRBQMsvNfDzceLxlBRa9cjMf3leXSiG+xCWn88WCnbQYNp+3pmziwPHEazy6Cx1NPJqj7XJFYCl4cBx0GQf+JeHEXvjpbvj9SUi4slwOp8mKXTFMWXeIFbticDhz728dd7s791e9H4DxW8bn2vNcDzTgn4hILqhSth6sh0SbjcjjkZQsmvUfKSIiIoXN4ZNJfLd0DwD9O9bA3X6F1xt3zoVZb8BR1ywBBFeCW9+Bah0hm3O9e7jZuP/GMtzbsDSzN0cxauFO1h+M5ccV+xi3aj931itJj9aVqBbmf2XZLiLEJyRH2+WqGrdDxdYw/x1Y9RVs/BV2zIFbh0D9Ry47IODMTZEMmraZyNjkjGUlAr0YeEdNOtQukSuR7696P99s+IZ1R9exOWYzNYvWzJXnKex05V9EJBcULVIKP4cTgJ0H1lkbRkREJA99NHsbKelOGlcIpl2N4hc2cDpgzxLY+Jvr85n74KO3wM/3uj6ObgGvINe96s+thOq3ZbvwP5fNZtChdhh/9mzB+Ceb0KpKMRxOk8n/HiL808U8+cMa1u679sF5y/mXw8128euqBgZhPmE0LN7wmp8rR3j6u6b/e2oehNWB5JMw9QUY2wmObrvoZjM3RfLszxGZCn+AI7HJPPtzBDM3ReZK3BCfENqXbw/AhK0TcuU5rge68i8ikhsMg2CHQYId9kdtA26zOpGIiEiu++9wLJP/PQTAgNtqYJxfsG+eCjP7Qdzhs8v8wiC0JuxeCKYTbO7Q+Gm46WXwCc6RXIZh0LxyMZpXLsaGgycZvWgXMzYdYe6WaOZuiaZxhWCea1OJ1lVDLsx8GYcTDvPMnGdId6Zn/dy49tevcb+8G+0/u0rdAE8thFWjXFMD7l8Oo1pAq77Qsi+4n52hweE0eXva5iyHNDQBAxg0bTPta4Zht135iZrL6Vq9KzP2zODv3X/T94a+FPEqkuPPUdjpyr+ISC4JcroDEHVyj8VJREREcp9pmgz9eyumCXfWK0m9MkGZG2yeCpMezVz4AyQccQ08ZzpdA9L1XAUd3suxwv98dUsHMfLhG5jbtzVdbiyDu91g9Z7jPDZmDZ1GLGXa+sPZvod954mddPu7G3vj9lLCtwSvNX6NUJ/QTG1CfUL5uM3HtCvXLjcO59rZ3aD5C64eFlVuBWcaLHqf9JHN2bBkKt8t3UO/3zYQ/ukijpx3xf9cJhAZm8zqPcdzJWa9kHrUCK5BqjOV33f8nivPUdjpyr+ISC4JNHyBWI4n5U4XOBERkfxk0fajLN15DA+7jVfCq2Ve6XS4rvhfYio8fIrBAz+6RqfPA5VC/Hj/vrq82L4q3y7ZzfjV+9kcGccLE/7lo9nbeOamStzTsBRe7lnnWRe9jp7zehKXGkelwEp81f4rQn1D6VKtCxHRERxNPEqITwgNizfMf1f8TzNNk2MJqeyIimd7lJPt3m9SNPBGHo0dSciJXdSd141t6TcxK/1hTnJ2fAQbThrbtlKck0QTxGpndZynrytHx1/8BMG1MAyDrjW68uayN5m0bRKP1XrskrdayIUMU8NQ54i4uDgCAwOJjY0lICDA6jgikg+89v3t/GXfR+vUEL54Kv9PpSMiInK1HE6T2z5bwraoeJ5qVYEBnc4bkG3PEvjh9svvqPtfUKFV7oS8jJOJqfywfB9jl+/hRKJrqt4Qf0+ebFmBrk3K4u/lntF26aGl9F3Yl6T0JOqG1GVk25EEegZakju7jp9KZXtU/OlCP8H1dXQCx0+lXtA2gFO86v4LXe3zsGGS6BbEovK9eXZTVcJtaxjo/iMljbNX+A+bwQxKe5RZzsY0rRjM23fWonpYztdEKY4U2v3ajpMpJ/m0zae0Ldc2x5+joLmSOlSnSkREckmwVyik7SPWTLA6ioiISK76be0BtkXFE+jtzvM3V7mwQUJU9naU3Xa5IMjHg97tqvDUTRWYuPoA3yzZTWRsMkNnbOXLBTt5tFl5HmtRntXR8xiwdADpZjotSrXg49Yf4+PuY1nu88UmpbEjKp5tUfHsOF3kb49K4FhCSpbtDQPKBvtQpbg/1cL8qBrqT5Xi/lQMuRfbkbUwrTc+0ZvpuHMQc73KUcncd0H/jTCOM8r9U55N68Os3Y3p8OkS2tcM5fmbK194+8c18LR7cm+Ve/lu03eM3zpexf8VUvEvIpJLigeUhZjVxBq50/1NREQkP0hMTWf47O0AvHBLZQJ93C9s5Bd64bKsZLddLvLxcOPxlhV4pGk5pqw7xOhFu9h19BRfLNjJdxt+wq34VMCkY4WOvNviXdztWRxvHohPTmNHdEKmK/nbo+KJisu6yAcoXcTbVdyH+lEt1J+qof5UCvHD2+MityWUaQzPLIbln8PCYVR2uAr/88fzsxngNGG4/wQ8yt7OX5uimbM5ijmbo7ipaggv3FKZRuVzZgyHLtW6MOa/Maw+spodJ3ZQpUgWJ5skSyr+RURySamilSEGTtgdVkcRERHJNd8s3kN0fAplgr3p1qxc1o3KNQevQEiOvcheDAgo6WqXT3i42bj/xjLc27A0s/47wnsrPuWk598ApJ1oTprng+w5lkzV0MzFv8NpsnrPcaLjkynu70XjCsHXNPp9Ymp6xhX8HdGni/wj8Ry+xOB7JQK9qBrqT9VQP6qcLvKrFPfD1/Mqyj+7u2v0/8DS8MdTF51x0WaAX0oUnzdPpnf71oxcuJMp6w6zePtRFm8/SpMKwbxwSxVaVC56xTMqZDo2vxLcUuYW5u6fy8StE3mz2ZtXva/rjYp/EZFcUql0XdgOJ+024hMT8PfxszqSiIhIjoqOT+arxbsA6NehOp5uF7mCfGIvpCVdZC+nC8EOw/JssL8rYeJk7anvMgr/MMdd7DjSlMlHIpm8LpJ2NUJ5tk0lbihXhJmbIhk0bTOR5xTmJQK9GHhHTTrULnHJ50lOc7Az+mw3/R1R8WyPjufA8Yu9blDc3/N0kX+20K8S6keAVy70RjCyOVFcQhSVK/jx8QP16dO2KqMX7+LXfw6was9xVn23ivplgnjhlsrcUr34VZ8EeKj6Q8zdP5dpu6fR+4beBHhozLXsUPEvIpJLypWojrtpkmYY7DiwkYbVmlkdSUREJEd9OncHiakO6pcJolOdixS3jnSY3AMcqRBSA1JiM0/3F1DSVfjXvDNvQl+BVEcqry99nVl7Z2FgMKDJALpU78KGgycZvWgXMzYdYe6WKOZuiaJKcT92RF84zs+R2GSe/TmCUY80pEPtEqSkO9h99FRGN/0zhf7+44lcbIbBYn4eVCl+tsCvFua6kh/k45HLr8A5sn3rRvGML8sW9eG9u+vwwi2V+Xrxbsav2s+6Ayd54od/qFEigBduqUyHWmHYrrBnRKOwRlQOqszOkzv5c8efPFrr0Sva/nql0f5ziEb7F5GstP+2FkfcbQwq14t72jxldRwREZEcsyMqnvBPF+M04dcezS5+T/eS4TBvMHgGwLPLXcX+vuWuwf38Ql1d/fPhFf/EtER6L+jNysiVuNncGNpqKB3Kd8jUZtfRBL5etJvfIw6Q7rz0/rzcbZQI9GL/8SQcF6nyg3zcM67inxl4r2qoH0X9PHPqsK6e0wGf1oa4SC45ZWNYXWjzGlTryPn3CByNT+G7pXv4acVeTqW6bousXNyP59pU4s56JXGzZ7N3ATBp2ySGrBxCGf8y/HX3X9iy2zOhkLmSOlTFfw5R8S8iWXng63ps8XTSw/92et4z1Oo4IiIiOeaJsWuYtzWaW2uG8vWjN2bdKHIDfHMLONOg8yio3zVvQ16lE8kneG7uc2yK2YS3mzef3vwpzUtefDyC6Rsi6Tk+Itv79/dyy9Rd/8wgfCF+ntd0P3yu2zwVJp25yn5uGWm4Hts9wXF6wMGwutCmP1S77YKTACcTUxmzbC9jlu0hLjkdcM048FybStzTsDQebpcv5BPTEmn3azvi0+L5su2X3FT6pms/vgJIU/2JiOQTAXgBiRw7ddDqKCIiIjlmxa4Y5m2Nxs1m0L9j9awbpSXD5GdchX/126HeQ3kb8ipFJkTyzNxn2BO7hyDPIEa2HUmdkDqX3CbdeZnL/qc927oS3ZuXJzQgnxf5F1PzTnjgR5jZL+tbN8o1d80MsPobOLIBJnaFsDrQuj9U75RxEiDIx4MX21flyVYV+GnlPr5dsof9xxPp/8dGPpu3gx6tK9GlURm83C/eI8TH3YfOVTrz0+afGL91/HVb/F+J67NvhIhIHgmyBQJwMuWoxUlERERyhtNp8t7fWwDo2qQsFUMuMqDtgnchejP4hsAdn11w9Tc/2n1yN91mdGNP7B5CfUL5ocMPly38AYr7e2Vr/zdVDSEs0KtgFv5n1LwT+myC7n/Bvd+5PvfZ6FruWwzaD3I9bvkiePjBkY3wy8MwuhVsmQbnnCjx93LnuTaVWdrvZt68vSbF/T2JjE1m4NT/aPn+Ar5atIuElPSLRnmo2kMYGCw7tIy9sXvz4OALNhX/IiK5qIhnMQBinXEWJxEREckZU9cfZuOhWPw83ejd9iJzrO9b7roCDK7C37dY3gW8ShuPbqT7zO5EJUZRIbACP9/2MxWDKmZr28YVgikR6MXFSnoD16j/jSvkzFz3lrPZoUIrqHOf6/P5Yzb4FoV2b7tOArR6yXUSIGoj/PIIfNXKdfvAOScBfDzceKJlBRa/ejPvdK5NqSBvjiWkMHTGVlq+P58R83YQm5R2QYwyAWVoVboVAL9s+yU3j7hQUPEvIpKLivmWAiCORIuTiIiIXLvkNAcfztoGwLNtKmU9EF1KvGt0f0yo/4iru3c+t/zwcp6Y/QQnU05Sp1gdfujwA2G+Ydne3m4zGHhHTYALTgCceTzwjprYr3BU+wLPJxjavnX6JMDL4OEPUZtgUjcY3RL++zPTSQAvdzuPNC3Hwlfa8OF9dalQzJeTiWl8PGc7LYfN58NZW4lJSMn0FF2ru8aR+HPnnySm6e+tS1HxLyKSi0oEVwLgpO3iXdZEREQKih+W7+XQySRKBHrxRMsKWTeaNQBO7oPAstAh/w92O3PvTHrO60lSehLNSjTj21u/pYhXkSveT4faJRj1SEPCAjPfAhAW6JUxzd91yycY2r4JfTbATa+4TgJE/we/dofRLeC/yZlOArjbbdx/Yxnm9m3NiIcaUC3Un/iUdL5csIuW7y/gnb82Ex2XDECzks0oF1COhLQEpu6aatURFgga7T+HaLR/EcnKxm2L6bqyJ26myZpH1uHmpnFWRUSkYDpxKpWbPlxAfHI6H91fj/tuKH1ho20zYUIXwIDu01xdwvOxX7b+wrur3sXEJLx8OO+1fA8Pu8c17dPhNFm95zjR8ckU93d19b/urvhfTuJxWDkKVo2GlNO3RobUgNavQs3OYMt8jdrpNJmzJYov5u9k46FYADzcbHS5sQzPtK7IoiOTGbZ6GBUDK/LnXX8W7DEVrpCm+rOAin8RyUpSYixNJrXANAx+v+UXqpapaXUkERGRqzJo2n+MWbaXGiUC+OuFlhcWtKdiYGRTOBUNzZ6H8HetCZoNpmkyesNoRq4bCUCXal14rfFr2M+/d11yV9IJ10mAlaMhxVXUE1L9nJMAmd8P0zRZtP0oX8zfyT/7TgDgZjO4o0ERlqX0IcmRyDe3fkPTEk3z+ECscyV1qLr9i4jkIm+fQIo4XOdYdx/aZHEaERGRq7Mv5hQ/r9wHwIDbalxY+Jsm/NXHVfiHVIdb3sz7kNnkNJ0MXT00o/DvUa8HA5oMUOFvBe8icPPrrtsB2rwGnoFwdCv89jiMbAYbfwOnI6O5YRi0qVacX3s0Y8JTTWlRuSjpTpPJa48Td6wuAF+v+9Gqo8n3VPyLiOSyIk7Xr9pDR7dZnEREROTqfDBzG2kOk9ZVQ2hZJYuR+zf+Clumgs0N7h4N7tmb+i6vpTnS6L+kPxO2TgCgf+P+9Kzf87rqJp4veQdBm/6nTwK8Dl6BcGwb/P6EqzfJhl8vOAnQrFJRxj3ZlN+fbc4t1YuTerw5AKujlvLYjzPZdPr2ADlLxb+ISC4LcrpGQo6OP2BxEhERkSu3dt8Jpm+MxGbAa7dVv7BB7CGY/rLr69b9oGSDvA2YTYlpibww/wVm7JmBm+HG+63e5+EaD1sdS87lHQRt+rlmB7h5wOmTANvhjyfhyyawYVKmkwAAN5QrwvePNWLqM/cQZNTCMEyWH/2L2z9fymNjVrN233FrjiUfUvEvIpLLAmx+AJxIPmJxEhERkStjmibv/b0FgPtvKEP1sPPuKXY6Ycpzrvu1S90ALftakPLyYlNieWrOUyw7vAxvN28+b/s5t1W8zepYcjFega77/vtshJvfAK8giNkBfzwFXzaG9b+AI/NMSrVLBTK4zTMA+BVbi82WxsJtR7l31Aoe+noly3ce43of7k7Fv4hILiviXhSA2PST1gYRERG5QrP+O8LafSfwdrfT99aqFzZY8y3sXghu3nD3V2DPf7PaRJ2K4rGZj7Hh6AYCPAL4uv3XtCzV0upYkh1egdD6FddJgFvecI0RELMTJj99+iTAxEwnAW4qfROl/EqRRgL970viwUZlcLcbrNgdQ9dvV3HvqOUs2Bp93Z4EUPEvIpLLivqEARBnnrI4iYiISPalpjsZNmMrAE+1qkBowHn38R/bAXPecn3dfjAUq5LHCS9vb+xeus3oxs6TOynuU5wfOvxA/eL1rY4lV8orAG46fRKg7VuukwDHd8HkZ+DLRrBuAjjSsdvsdKnWBYDZB39n6D11WPjKzXRvVg4PNxsR+0/yv7FruP3zpczcFInTeX2dBFDxLyKSy4oHlgPgpC3V4iQiIiLZN37VPvbGJFLMz4OnW1fKvNKR7iq80pOgYhto9KQlGS/lv5j/eHTGo0SeiqR8QHl+6vgTlYtUtjqWXAtPf2j10umTAAPBOxiO74Y/e7hOAvw7jnsq3omX3YttJ7bxb/S/lAryZtBdtVn66s08fVNFfDzs/Hc4jh4/RxD+6WKmrDtEusOZ6WkcTpMVu2KYsu4QK3bF4CgkJwkM83rt85DDrmR+RRG5vqxYN42n17+Or9PJisc2aURhERHJ9+KS02j9wQJOJKbx7t21ebhJucwNFn0IC95xTc323HIILG1N0ItYFbmKXvN7kZieSM2iNRnVbhTBXsFWx5KclhLvuvVk+eeQGONaVqQCb1esxe/HNxBePpyPWn+UaZPjp1IZs2wPY5ftJT7FdctA+aI+PNemMp0blGL+1igGTdtMZGxyxjYlAr0YeEdNOtQukWeHll1XUoeq+M8hKv5F5GKOHTvAzdNdgwrNvmM2JYLz338cIiIi53p/5lZGLdxF5eJ+zOzdCjf7OR2GD/8L37YDZzrc/TXU62Jd0CzM2TeHfov7keZMo0lYEz675TN83X2tjiW5KSXh9EmAEZAYwzYPd+4rVQI7BrPumUGof6kLNolNSuOnFXv5bukeTiSmARDs48HxxAt7ap65bDPqkYb57gTAldSh6vYvIpLLihUtja/T1Z1s54H1FqcRERG5tEMnk/hu6R4A+neonrnwT0uCP55xFf4174K6D1iUMmu/bf+Nlxe9TJozjXZl2zGy3UgV/tcDTz9o2Qd6b4D2g6nmFkjD5GQcmEwa1wEifgRHWqZNAr3def6WKiztdwsDbqtBUd+sC3+AM1fLB03bXKBvAVDxLyKS2wyD4HTXOeP9UVstDiMiInJpw2dtIzXdSdOKwbStUTzzyvnvwLFt4FscOn0C+eRWNtM0+WbDNwxaMQin6eTeKvfyUeuP8LB7WB1N8pKnH7ToDX020LV8JwB+83CQOvUF+LwhrP0B0jMX+L6ebjx1U0U+7lLvkrs2gcjYZFbvOZ5b6XOdin8RkTwQZLoDEHVyj8VJRERELm7ToVgmrzsEwIDbamYep2bPEljxpevrOz8H36IWJLyQ03TywZoPGPHvCACeqvMUA5sNxG6zW5xMLOPhyy3tP6S4dwjH7XZmBYfByf0wrRd8fgP8M+aCkwAnE9MusrPMouOTL98on1LxLyKSBwJxdTmMSYy0OImIiEjWTNPkvb+3YJpwV/2S1CkdeHZlchz8+RxgQsNHoVoHy3KeK82ZxoClA/h5y88AvNroVXo17KXBdQV3mzsPnJ72b0LFBhD+nqvHSux++KuPqyfAP99nnAQo7n92KksbTpraNnOnbTlNbZuxcXY2gHPbFTQq/kVE8kCgm+sPqNi0GIuTiIiIZG3htqMs3xWDh93Gy7dWy7xy5muuoimonKuIygeS0pPos6APf+3+C7th572W79GtZjerY0k+cl/V+3C3ubMx5j82Vr4Jeq+H8KHgFwqxB+CvF10nAdZ8R+MyvpQI9KKDbTVLPXsx0eMdRnh8wUSPd1jq2YsOttWUCPSicYWCO2uEin8RkTxQ1DMUgDhnvMVJRERELpTucPLe31sA+F+L8pQJ9jm7cut0WPczYMDdo11zrVssNiWWZ+Y8w+KDi/GyezHilhHcUekOq2NJPlPUuygdyrt6qUzYOgE8fKDZc66TAB2GgV+Y6yTA9L7Yv7iBX0pOZKT7p4SR+b7+MI4z0v1TRjY8iN1WcHuVqPgXEckDIQFlAYg1Cu59YiIiUnj9uvYgO6ITCPJx57mbK59dkXAUpvZyfd38BSjX3JqA54hOjOZ/s/7Hv9H/4u/hz9e3fs1NpW+yOpbkUw9VfwiAmXtnEpN0ugemuzc0fRZ6r4MO77tOAsQdpOyeSRgGnF/f2wwwDIMG/70PTkfeHkAOUvEvIpIHShV1/SF1wl5w/8MQEZHC6VRKOh/P2Q7AC7dUIdDbNUgtpum6NzrxGBSvCTcPsC7kafvj9vPojEfZcWIHId4hjO0wlgbFG1gdS/KxOiF1qFOsDmnONH7b/lvmle7e0LSHqydA4x4AXOy6voEJcYdg3/LcDZyLVPyLiOSByqXrAnDSbpCQdMriNCIiImd9s2Q3R+NTKFfUh25Ny51dsX4ibP0LbO5w91fgbu1AZ1tittBtRjcOJRyijH8Zfuz4I1WLVLU0kxQMZ67+T9o+iTRnFqP6u3tBmUbZ21lCVA4my1sq/kVE8kDZktVxM01Mw2D7gY1WxxEREQEgOi6ZrxfvBuDV8Op4uJ0uD04egBmvur5u0x9K1LUoocuaI2t4fNbjHE8+TvXg6vzY8UdK+5e2NJMUHOHlwwn2CiY6MZr5++dn3cgvNHs7y267fEjFv4hIHrC7eVAs3QRg7+H/LE4jIiLi8snc7SSmOmhQNojb6oS5Fjqd8OezkBIHpRtBiz6WZpy3fx495vQgIS2BG0Nv5Pvw7ynmXczSTFKweNg9uK/qfQCM3zI+60blmkNASS7V8Z+AUvli3IurpeJfRCSPBDndAIg8scviJCIiIrA9Kp5f1hwAYMBtNTCM00XP6q9g7xJw93F197e7WZZx8o7J9F3Yl1RnKjeXuZnR7Ufj72H9bANS8DxQ9QHcDDcioiPYdnzbhQ1sdtfgf8CFJwBOP+4wzNWugFLxLyKSRwJN172SMQkHLU4iIiICw2ZsxWlCh1ph3Fj+9NzlR7fB3LddX986BIpWsizf95u+563lb+E0ndxd+W4+bvMxnnZPy/JIwRbqG0rbcm2B09P+ZaXmnfDAjxBQIvPygJKu5TXvzOWUucu603giIteZQHsgkMiJlKNWRxERkevc8p3HmL81GjebQb+O1V0LHWkw+RlIT4ZKbeHGJyzJZpomH6/9mLH/jQXgf7X/x4sNXzzbM0HkKj1U/SFm7Z3F9N3TefGGFwn0DLywUc07oXon16j+CVGue/zLNS/QV/zP0JV/EZE8UsSjKABxzliLk4iIyPXM6TR59+8tADzcpCwVivm6ViwZDof/Ba9AuOsLsKDYTnem8+ayNzMK/5dueIm+N/RV4S85omHxhlQrUo1kRzJ/7Pjj4g1tdqjQCurc5/pcCAp/UPEvIpJnQvxcoxLHkmhxEhERuZ5NWX+I/w7H4e/pRq+2VVwLD62FRR+4vu708emBz/JWcnoyLy58kSm7pmA37AxpMYTHaj+W5zmk8DIMg641ugLwy7ZfcDgdFifKWyr+RUTySIkiFQE4aUu3OImIiFyvktMcfDRrOwDP3lyJon6ekJYEfzwDpgNq3eO62pnH4lPj6TG3BwsPLMTT7sknbT6hc+XOeZ5DCr/bKtxGoGcghxIOsfjgYqvj5CkV/yIieaRciRoAHHcDR/r1daZZRETyhzHL9nLoZBIlA714vEUF18K5gyBmB/iFQafhuZ7B4XSw5sga/t79N2uOrCHqVBT/m/k/1katxc/dj9HtRnNz2ZtzPYdcn7zcvLin8j0AjN96kWn/CikN+CcikkeqlKkPqyDNMNh9ZAdVSle3OpKIiFxHjp9KZeSCnQC8HF4NL3c77F4Eq0a5Gtz1BfgE52qGufvmMmz1MKISozKW2Q07DtNBUa+ijG4/murB+v9RcleX6l34YfMPrIxcye6Tu6kYVNHqSHlCV/5FRPKIj28QRRxOAHYf3GRxGhERud6MmLeD+JR0apUMoHP9UpAcC38+51p5w/+gSvtcff65++bSd2HfTIU/gMN09YbrUbeHCn/JE6X8StG6dGvgEtP+FUIq/kVE8lCww/Vr9+CxrRYnERGR68meY6f4eeU+AF6/rQY2mwEz+kHcQShSAW59J1ef3+F0MGz1MEzMi7b5btN3190AbGKdMwP/Td01lYTUBIvT5A0V/yIieSjA6QHAwqjVjF07l9T0wjX4X2pqCpPmjOCL319m0pwRpKamWB1JRESAD2ZuJd1p0qZaCC0qF4PNU2H9BDBscPdo8PTL1eePiI644Ir/+Y4kHiEiOiJXc4ic0SSsCRUDK5KYnsiUXVOsjpMnVPyLiOSRD5f8yiaPNADWue1h+KYXufGHNny45Fdrg+WQr6cMIPynhgw5/A1fJcxiyOFvCP+pIV9PGWB1NBGR69rafceZsekINgNe61gDEqLhrz6ulS16Q9mmuZ7haOLRHG0ncq0Mw+Ch6g8BMHHrRJym0+JEuU/Fv4hIHvhwya/8uGswaUbm/1hM+0l+3DW4wJ8A+HrKAL44MYVjdiPT8hi7wRcnphSqEwAOp8mKXTFMWXeIFbticDgv3oVVRMRqpmny7vQtADxwYxmqhfrB1F6QGAOhtaHNa7meIT41npl7Z2arbYhPSC6nETnrzkp34ufux964vaw4vMLqOLlOo/2LiOSy1PR0Jm3/EOwmGJmLYwwDwzSZtP0jeje7Gw+3/Pdr2TRNTEycphPTNHHiPPu16SQ5NYnxx/7EtBsXHJ95+vgmHvuTx1LfwsPD06KjyBkzN0UyeOp6ijrm4ON2jMT0YsTY2/PWnfXoULuE1fFyRGpqCn8u+orouP0UDyhL59bPFPj3TeR6NmPTESL2n8Tb3U7f9lVh3TjYPgNs7nD3V+CWez/fpmkyZ98chq0extGkS1/RNzAI9QmlYfGGuZZH5Hw+7j7cVfkuxm0Zx/it42lRqoXVkXJV/vsrU0SkkBm/bj7JbkmAkeV60zBIdkuk8/ct8cUdE+fpAZHO/ntmgCTTMDHPWZLps8F56860dy13Ziw7u50z4zFnH2PiNDinfda5M3G7eEcy0zA46mYQ/tON+OOOF2544o6X4YGXzRMvuxdedh+83Xzw9vDD1yMAX89A/LyD8PcJJtCnKIH+IRQNKI6fdwDuNvfsvOw5buamSL6b+iaeoSvYe87xFkufyXdTmwFDCvwJgK+nDGDCsT85dub4EmDUT1/xULHOPH3Xu9aGE5Erlpru5P2ZrgFmn76pIsUdUTCjv2vlLQMgrHauPXdkQiTvrnqXRQcXAVAuoBydKnRi1HrXtILnDvxnnP7/sV/jftht9lzLJJKVB6s9yLgt41hycAkH4g5QJqCM1ZFyjYp/EZFcduJQ9rqRHfA8lctJsisbxf5VOOYGx0gD0oCksyucpz/OW3wxHk4TLxO8nAaepoGXaccDO56mG+6GB56GBx6GF542bzzs3ni5+eHp7oe3ewDeXkH4egXh51UUP79g/H2LE+AbjI+XB97udrzc7Xi62TDOO+HhcJpMmDWIbSVXYnLhrQ0xJVcyYdYg2tcchd2WO69fbjtz64Z5kVs3mIJOAIgUMONW7WNfTCIh/p483ao8TOgMqfFQpik075Urz5nuTGf8lvF8se4LktKTcLO58UTtJ3iq7lN42j2pUqQKw1YPyzT4X6hPKP0a96NduXa5kknkUsoHlqdFqRYsO7SMidsm8kqjV6yOlGvydfH/9ttvM2jQoEzLqlWrxtatrjOYycnJvPTSS0ycOJGUlBTCw8MZOXIkoaGhGe3379/Ps88+y4IFC/Dz86N79+4MHToUt3O61i5cuJC+ffvy33//UaZMGd544w0ee+yxPDlGESn8yrllrxi8K9mP0r5lsGEHDGyGqwg1sGMYNgxsGDbXZ9uZx8bpdRmPzy6zZayzYzNscM7js+vdzmljh9PLsdlPr7djM9zAZsdu2E/vw45hO/3ZsBOxbQpfpv192eO7J6UKxbzLkpyeQKrjFKnOJNeHmUKamUqqkUYq6aQaDlIMJ8k2JykGJNkg0bCRerqoTrUZpAJxdnD1TUg//ZECZHEC5czqJCDuwtU208TbNPFygpcTPJ02PEwbHqYdd9MNd9MNw3Rja3CMq/DP4tYGTJOdRZYw4Ksn8fVyB2zYDE6/f2AYrivprkOwuQbcMYxz1nP6PQSb60FGm3Mfu74HXFfJDMPAhuGKc7qd7XQ+1zac/j45u0/D4PQ2rjZnntfpSOenmD8xbYX/1g2R60VsUhqfzdsBwIvtquIb8TXsWwbuvnD3KMiFK+z/xfzHoOWD2HLcNcZAw+INeavZW1QKqpTRpl25dtxc5mYioiM4mniUEJ8QGhZvqCv+Yqmu1buy7NAyJu+cTM/6PfFx97E6Uq7I18U/QK1atZg7d27G43OL9hdffJHp06fz66+/EhgYyPPPP88999zDsmXLAHA4HHTq1ImwsDCWL19OZGQkjz76KO7u7rz33nsA7Nmzh06dOtGjRw/GjRvHvHnzePLJJylRogTh4eF5e7AiUijdXv0mRi6cQLTdnmUXesM0CXU4eKvdUDwqtcn7gNeofp0b+OWnv4ixGxc9vmIOkwGPTbiqwtF0OklJSSI+/jix8Uc5mXCMhMQY4pNOcCrlJIkpcSSlxpOUlkCSI5EURxLJzmRSnCmkmGmkkE4K6SQbDpINk2TDdJ1QsLkKcqdhcMowOJXpzoVzuyOccYkxcg2DOLvBdJ/VFzmI8z7nR/bL37rx56KveKB97lwtFJGcNXLhTk4mplG5uB8PlI2Hbwe7VoS/C8EVc/S5EtMS+fzfzxm/dTxO04m/hz99b+jLPVXucZ18Po/dZqdRWKMczSByLVqWakkZ/zIciD/AX7v/4oFqD1gdKVfk++Lfzc2NsLCwC5bHxsby3XffMX78eG655RYAxowZQ40aNVi5ciVNmzZl9uzZbN68mblz5xIaGkr9+vUZMmQI/fr14+2338bDw4PRo0dToUIFhg8fDkCNGjVYunQpn3zyiYp/EckRHhVa0XeaSf8gVyF8boFsmK5q8MV4E48KrSxKeG08PDx5qFhnvjgx5aLH92Cxzld9xdiw2fDy9sXL25eQ4jl3H57TkU5y8gkSk2I4lRhDXGIMsaeOE590gvjkWBJS4jiVmkBi6im2xu/kH/fEy+6zTBoE4jrOjHEazmtzwXLz3HMDF9vm3LVZnUO4cN3Fnvf8dSaQZHMQc4lxG86Iit132TYiYr2DJxIZs2wvAAPCK+I2pSs4UqBye7jhsRx9rkUHFvHuqneJPBUJQMfyHXm18asU8y6Wo88jkptsho0Hqz3Ih/98yIStE7i/6v0X3AJYGOT74n/Hjh2ULFkSLy8vmjVrxtChQylbtixr164lLS2Ndu3O3htUvXp1ypYty4oVK2jatCkrVqygTp06mW4DCA8P59lnn+W///6jQYMGrFixItM+zrTp06fPJXOlpKSQkpKS8TguLou+pCIiADY7t936Ae5/PcP7RYOIOqcHU3GHg34xJ2l/+1e50gUzrzx917swhdODxZ39z7KYw+TBfDpYnM3uho9vCD6+IVzuT9SVEd/yz8bPLrvPtxr2pmnDJ3MmYB6aNGcEQw5/c9l2f52cQ7ElI3igRU910RXJxz6atY3UdCfNKhalzZGxcGQDeBeBu764cNaZqxSdGM2w1cOYs28OAKX8SvFG0zdoWapljuxfJK91rtKZL9Z9wc6TO/kn6p9C2Tvl8qf5LdSkSRPGjh3LzJkzGTVqFHv27KFVq1bEx8dz5MgRPDw8CAoKyrRNaGgoR44cAeDIkSOZCv8z68+su1SbuLg4kpIuPvLU0KFDCQwMzPgoU6bwjgopIjmg5p20v/0rZsbC95FRvB99jO8jo5gZa7gK/5p3Wp3wmj1917vM6hbBmyWf4hm/cN4s+RQzu0Xky8L/SjWq+yjFHWZGT4bzuW7dMGlU99E8TpYzOrd+hmLpzoseH6YJpslhd5P3dn9D+JgbGDl3CMnpyXkbVEQua+PBWP5cdxiAITcmYSz92LWi08fgf2Fv2ivlNJ38svUX7vrzLubsm4PdsPO/Wv/jjzv/UOEvBVqARwC3V7wdgPFbxlucJnfk6yv/HTt2zPi6bt26NGnShHLlyjFp0iS8vb0tTAavvfYaffv2zXgcFxenEwAicmk178Steica7VsOCVHgFwrlmhfoK/7n8/DwLJT3hNvdPHit6sP03Tnuorc29K/6MHY3D6siXpPs3LrxiNGY46d2s8wrmig3GHVoEuN+/JXbQsN5vt0bBHoGWhVfRE4zTZP3/nYNtvdAvaJUXvocmE6ocz/Uvuea97/9xHYGrRjEhqMbAKhdtDYDmw+kenD1a963SH7wUPWH+HX7r8zbP4+Ze2biNJ2FalDKfF38ny8oKIiqVauyc+dO2rdvT2pqKidPnsx09T8qKipjjICwsDBWr848+FJUVFTGujOfzyw7t01AQMAlTzB4enri6akRj0XkCtnsUEDv7b/etWv5Gh8Dw7aPI+qc//9DndCv6sO0a/maZdlyQnZv3di4aRVTF7/JIo8DRLq7MfHYTCaPn0mboBb0bfcmJf1LWXUIIte9BduiWbE7Bg83G295/QLHd4F/Cbjtw2vab3J6MqPXj+aH/34g3UzH192XXg160aVal0JREImcUaVIFSoHVWbnyZ28svjslH+hPqH0b9y/wE9HWaCK/4SEBHbt2kW3bt244YYbcHd3Z968edx7770AbNu2jf3799OsWTMAmjVrxrvvvkt0dDTFixcHYM6cOQQEBFCzZs2MNn//nXmKqjlz5mTsQ0RE5Ix2LV/j5qYvEbHxJ47G7SckoCwN63QrsFf8z/f0Xe/yWOpb/LnoK6Lj9lM8oCydWz+TabDGOrWbUKf2bB7Z9R9TZvdjkX0H2z09mBW3jLm/d6CxT11euuUtqhWrZuGRiFx/0h1O3vvbNR32oNpR+K3/3rXiri9d9/tfpeWHlzNkxRAOJhwEoG3ZtvRv3J8w32u/hUAkv5m7by47T+68YHl0YjR9F/bl4zYfF+gTAIZpXuwGP+u9/PLL3HHHHZQrV47Dhw8zcOBA1q1bx+bNmwkJCeHZZ5/l77//ZuzYsQQEBPDCCy8AsHz5csA11V/9+vUpWbIkH3zwAUeOHKFbt248+eSTmab6q127Nj179uTxxx9n/vz59OrVi+nTp1/RaP9xcXEEBgYSGxtLQEBAzr8YIiIi+dCRAzuYMb0/S9jIGu+zJwlqeVSiz02v0aRk40I5YrJIfjN+1X5en7yRst4pLPR9HVtCJDR6EjoNv6r9xSTF8OE/HzJ993QAivsUZ0CTAdxS9pacjC2SbzicDsJ/DycqMSrL9QYGoT6hzLx3Zr7q8XIldWi+vvJ/8OBBHnroIWJiYggJCaFly5asXLmSkJAQAD755BNsNhv33nsvKSkphIeHM3LkyIzt7XY7f/31F88++yzNmjXD19eX7t27M3jw4Iw2FSpUYPr06bz44ot89tlnlC5dmm+//VbT/ImIiGRDWJkq/K/H73Q+so/5U19nadoq5vt68V/qLp6a+yTl7CV4rtlLhFdol6/+WBIpTE6lpPPxnO0AjAn9FduRSAiuBO0HX2bLC5mmyZ87/2T42uHEpsRiYNC1RldeaPACvu6+OR1dJN+IiI64aOEPrilzjyQeISI6osDOBJCvr/wXJLryLyIiAgnHDrJsykBWJC7kLz8vUmyuiYWKGUE80bAn99e4G0+7xswRyUkfz9nOiHk76B74L4NSPgTDBo/PhjJXVqDsid3D4BWD+SfqHwCqFanG283fpnax2rkRWyRf+Xv33/Rb0u+y7d5v9T63VbwtDxJlz5XUofl6qj8REREpWPyKlSb8ie/o3205n9lu5n8nThHgcHDMPMn7a9+lzc83MeKf0cSlxlkdVaRQiIpL5pvFuwnhBAPMb1wLW/a9osI/1ZHKqHWjuHfqvfwT9Q/ebt68dMNLTLx9ogp/uW6E+ITkaLv8SFf+c4iu/IuIiFwoLf4Ym6a+R0TUZCYGenHEzXXHoYfpzp2V76dHg8cJ9Q21OKVIwdX/9w1MXLOf3wI+5cbUNRBWB56cD9kciPSfI/8weOVg9sTuAaBlqZa80fQNSvlp5g65vpy55z86MRqTC0vkwnDPv4r/HKLiX0RE5OKcp06w/a8P2XhgPOMDPdnp4SpMbKaNNqXD6XXjM1QKqmRxSpGCZduReDp+tpj7bQt43/0bsHvA04sgtOZlt41NieXjtR/zx44/ACjqVZT+jfsTXj5cg3TKdWvuvrn0XdgXINMJAAPXz0R+HO1fxb8FVPyLiIhcnpl0kr0zPmX7zrFMDHDnH2+vjHUNizWnT6MeNCjewMKEIgXHY2NWs2v7JuZ4vY6XmQTth0CLXpfcxjRNpu+ZzodrPuR48nEA7qt6H30a9iHQMzAvYovka3P3zWXY6mGZBv8L8wmjX+N++a7wBxX/llDxLyIicgVS4jk4+3MO/vcNvwTYmOfjjXn6amNl/5q8cOMztCnTBpuh4YlEsrJs5zG6fbuCXzyG0Mi2Dcq1gO7T4BLdkQ/EHeCdVe+w/LBrWuxKgZV4q9lbNAxtmFexRQoEh9NBRHQERxOPEuITQsPiDfNVV/9zqfi3gIp/ERGRq5B6iqj5Izn670h+84Op/r6knT4JEOpVhucaPMntlW7Hw569+5dFrgdOp8ntny+lZfQ4XnefAB5+8OwyKFI+y/ZpzjR++O8HRq8fTYojBQ+bB8/Ue4b/1fof7nb3vA0vIjlKxb8FVPyLiIhcg7QkYhZ/zclVI5jmm8Ykf3/i7a6r/gFuwTxetztdqj2An4efxUFFrPf72oN8/ds0pnm8gYeRDnd+Dg0fzbLt+qPrGbRiEDtO7ACgSVgT3mz2JuUCyuVlZBHJJSr+LaDiX0REJAekJRO7YgyJSz9mtlcSPwb6E316hgBPmw8PVu9C91rdCvRUSyLXIjnNwa0fzmF08qvUtO2Dqh3goYlw3iB98anxfBbxGZO2TcLEJMgziFcavcIdFe/QgH4ihYiKfwuo+BcREclB6SmcWv0TSYs+Yol7HGMDA9jt4eqebMeN2yvdwRN1/keFwAoWBxXJWyMX7sScO4ieblMxvYMxnlsJ/menyzRNk7n75zJs1TCik6IBuLPSnbx848sU8SpiVWwRySUq/i2g4l9ERCQXONJIjhhP0vwPWE8M3wf586/XmRkCDG4q1Yan6z1BvZB6lsYUyQsxCSn0/vArfuAt7IYJD/wINe/KWB+ZEMl7q95j4cGFAJQLKMebTd+kSYkmFiUWkdym4t8CKv5FRERykSOd1PWTSJ47jF2OSL4PDGChr0/G6rrFGvJMvSdoWaqlZgiQQuvdP1bz8LpHKG+LwqzbBeOerwHXyOTjt47n838/Jyk9CTebG4/Xfpyn6z6Np93T4tQikptU/FtAxb+IiEgecDpwbPydU3OGcixlP2MDA5jm50v66XuYy/lX5Ol6T9CxfEeNYi6Fyu6jCawY0Z2H7XNJ8QnD84VV4B3E5pjNDFoxiM0xmwFoULwBA5sNpFJQJYsTi0heUPFvARX/IiIiecjpxLl5CqfmvEdiwi7GBfjzS4AfiTbXVf+iXsV5vHZ37q16L77uvhmbFaS5m6/W9XCM16MRX42mV2Q/14Nuf5JYtglfrvuSn7f8jNN04u/uz4s3vsi9Ve5V7xeR64iKfwuo+BcREbGA04m59S9OzRmKM3YLv/r78VNAADFurmLX182PrjUeomuNrqyLXsew1cOISozK2DzUJ5T+jfvTrlw7q44gR83dN7fQH+P1KGLbbkqNv5lQ4yQn6/yPDU3u4Z2V7xB5KhKADuU70K9xP4p5F7M4qYjkNRX/FlDxLyIiYiHThO0zOTXnPdyPbWCany/fBway38M1TaANN5yku9qdO83Z6ceftPmkwBfHc/fN5cWFL4IJnDuT2+nHheEYofD3bDj/+BqENGDFB/fQKmURW7zL8k2jW5hzYD4AJX1L8kbTN2hVupXFqUXEKir+LaDiX0REJB8wTdg5j6S57+IZFcECH2++CQzkPy+PS24TZPfjp7sm4m5zx4YNwzCwGTZshg2Dc742DGyc87Vhy9TewLBkDnWH00Hrie2ITT2WufDPOEYI8ghh4YNzCnShXNh7NmR1fEVsfrwRuYdjNjdGhJXglCMZu2Hn0ZqP0qNeD3zcfS6xRxEp7FT8W0DFv4iISD5imrB7Iclz32PDifU8USL08tvkkHNPFmR58uC8EwbZOdlgGAZ2w37RExAJqQnsOLnjstluLRdO+cByeNo98bR74mH3yPT5gmW2rNtZcQLhbM+Gwtl7Y+6+ufRd2BeT8/40P/On+uljrl20NgObD6R6cPU8Tigi+ZGKfwuo+BcREcmHTJOxv/dk+Kkll21qd7oKdxNXT3kMVxlm5v2F/HzPzXC74ITARU8gXMuy0ycf7DY7XSbfw0lHQubC/zTDNCliD2D+w0sKZM8Gh9NB+O/hma74n8/A4JVGr9C1etcCeYwikjuupA51y6NMIiIiInnPMHCzZ28QtG+iomiUnJLlOhNwnv4wAYdhnF1mgIlxdr0BztOPzdPrXV8b57Q/p00W651n1hvnPrdxTtvM67e7uzMyOOiyx9gh4RTBDicphkGqYZBi4PpsM85ZZpCCa1mqYctok35O0Z1uppOenk5iemK2Xtscc5FbKkzD4LgznmbjmuBuc8NmGBm3YBhnbsfg9Nfn3aLhWna2BwZw3nojozdGxjLOLsMg6/Wne2hgkLHvc9efaY8Bscmxlyz8AUxMqgdXV+EvIldNxb+IiIgUavVCGhF6/Fei7XbMi1w1DnU4KBPaDkpWcC00na7u1ubpstw0sZtO7JgXrHN9bWKajtOfTUzTefaz0wTTgXl6m4zPTlcfA/P0/lyfz67P/LV59utznvPMugZJMfyenn7ZY3z72Cnshg2b6cBmOnAjPduvowPXSYCMEwRnThxkWmZc4bKsTz6c/znJMHBmYyyFJGcKSc6sT+AUBkdPXfoEgYjIpaj4FxERkUKtZtOO9Fjel8HF7Rimmak4Nk7f/fh0TDohfX8Et6v/08g473NecuxeTP/fHqBv8WIXPcZ+MSfw6vYr9oo3nd3wzAkEZzo40lyfnQ5wnvk6HUd6OqmpyaSlppGSlkpaaiqppz+npaeRnpqKIy0N0lNxS0+D9DTsaal4pqfh40jDkZ6OMz0NpyMNpyMd05GOmZ7q+uxIx3SmYzhdn3GmY3Omg5mOG07ccH0+6R3NO6Uu/zr0jzpFxVQbBk5smBg4sOPEMExsODAwsRsOOK/XBZztTWGe7k1hZixz9co4f7mrV4dx4TLIeP2d5yzjnB4imfcBe9zd+Snw8reNBsceufyLICJyESr+RUREpFCzu7lRqd4bfLSuHx8UK0LUOQV+cYeDV4+dIKT++9ivofC3mr18C1qYgXwUdeyCYwx1OHjl2AlaEIi9fIvMGxoGGHaw2cHNM+t9A96nP/KK02mS6nCSnOYgJd3JtpV/EXrgzcv2bEgKe4v15VqRmu4kzeEkNd1JyjlfpzpcX6elpZOWnoYz3XVywuFIw5mejtORhsPhcJ2oOP3YdXLCgf30iQQ7DtyM05/PPM5Yd85yIzvrXcsbGvuZ7XvssscXGKMBKETk6mnAvxyiAf9ERETyt39n/UDoikEc8krgqN1OiMNByWQ/opsNpEF4d6vjXbvNUzEnPUo6Juu8PDOOsX5yCm4YGA/8CDXvtDrlVXGkpzP54xoMLu46QZFVz4Y3o1O4p++WXDmJ43CarhMIZ04iXObkwtnl5un2DtfXjvPan/7a8+ByWqW8Td/ixS56fB9HH8OrwShatr87x49PRAoujfZvARX/IiIi+Z8jPZ2tq2aRdOIQ3kVKUb1JeIG+4n+BzVMxZ/bDiDucscgMKIXRYViBLfzP+HfWDxzNovdGaHp6Ru+NgnoSZ8WOaMr93IRNPkkXHF9YejqvHDtB7UQf9j2ykmZViluYVETyGxX/FlDxLyIiIvmC0wH7lkNCFPiFQrnmrm79hUBh7b3hcJoMeO893kv7AAewzvucnhtJKdiB191f5d3XX8duU9d/ETlLxb8FVPyLiIiI5L7C2ntj5qZI/hw/mrfcf6SkcTxj+WGzKIPTutG5aw861C5hYUIRyY9U/FtAxb+IiIiIXIuZmyIZMnUjZRLWU5yTRBPEAb96vHlnHRX+IpKlK6lDC/5pUhERERGRQqBD7RK0rxnG6j03EB2fTHF/LxpXCFZXfxHJESr+RURERETyCbvNoFmlolbHEJFCyGZ1ABERERERERHJXSr+RURERERERAo5Ff8iIiIiIiIihZyKfxEREREREZFCTsW/iIiIiIiISCGn4l9ERERERESkkFPxLyIiIiIiIlLIqfgXERERERERKeRU/IuIiIiIiIgUcir+RURERERERAo5Ff8iIiIiIiIihZyKfxEREREREZFCTsW/iIiIiIiISCGn4l9ERERERESkkFPxLyIiIiIiIlLIqfgXERERERERKeRU/IuIiIiIiIgUcir+RURERERERAo5Ff8iIiIiIiIihZyKfxEREREREZFCzs3qAIWFaZoAxMXFWZxERERERERErgdn6s8z9eilqPjPIfHx8QCUKVPG4iQiIiIiIiJyPYmPjycwMPCSbQwzO6cI5LKcTieHDx/G398fwzCsjnNRcXFxlClThgMHDhAQEGB1HLkKeg8LPr2HBZ/ew4JN71/Bp/ew4NN7WPDpPcwfTNMkPj6ekiVLYrNd+q5+XfnPITabjdKlS1sdI9sCAgL0Q1rA6T0s+PQeFnx6Dws2vX8Fn97Dgk/vYcGn99B6l7vif4YG/BMREREREREp5FT8i4iIiIiIiBRyKv6vM56engwcOBBPT0+ro8hV0ntY8Ok9LPj0HhZsev8KPr2HBZ/ew4JP72HBowH/RERERERERAo5XfkXERERERERKeRU/IuIiIiIiIgUcir+RURERERERAo5Ff8iIiIiIiIihZyK/+vMl19+Sfny5fHy8qJJkyasXr3a6kiSTUOHDqVRo0b4+/tTvHhxOnfuzLZt26yOJVdp2LBhGIZBnz59rI4iV+DQoUM88sgjFC1aFG9vb+rUqcM///xjdSzJJofDwZtvvkmFChXw9vamUqVKDBkyBI19nH8tXryYO+64g5IlS2IYBn/++Wem9aZp8tZbb1GiRAm8vb1p164dO3bssCasZOlS72FaWhr9+vWjTp06+Pr6UrJkSR599FEOHz5sXWDJ5HI/g+fq0aMHhmHw6aef5lk+uTIq/q8jv/zyC3379mXgwIFERERQr149wsPDiY6OtjqaZMOiRYvo2bMnK1euZM6cOaSlpXHrrbdy6tQpq6PJFVqzZg1fffUVdevWtTqKXIETJ07QokUL3N3dmTFjBps3b2b48OEUKVLE6miSTe+//z6jRo3iiy++YMuWLbz//vt88MEHfP7551ZHk4s4deoU9erV48svv8xy/QcffMCIESMYPXo0q1atwtfXl/DwcJKTk/M4qVzMpd7DxMREIiIiePPNN4mIiOCPP/5g27Zt3HnnnRYklaxc7mfwjMmTJ7Ny5UpKliyZR8nkamiqv+tIkyZNaNSoEV988QUATqeTMmXK8MILL9C/f3+L08mVOnr0KMWLF2fRokXcdNNNVseRbEpISKBhw4aMHDmSd955h/r16+sMeQHRv39/li1bxpIlS6yOIlfp9ttvJzQ0lO+++y5j2b333ou3tzc///yzhckkOwzDYPLkyXTu3BlwXfUvWbIkL730Ei+//DIAsbGxhIaGMnbsWB588EEL00pWzn8Ps7JmzRoaN27Mvn37KFu2bN6Fk8u62Pt36NAhmjRpwqxZs+jUqRN9+vRRz8Z8Slf+rxOpqamsXbuWdu3aZSyz2Wy0a9eOFStWWJhMrlZsbCwAwcHBFieRK9GzZ086deqU6WdRCoapU6dy4403cv/991O8eHEaNGjAN998Y3UsuQLNmzdn3rx5bN++HYD169ezdOlSOnbsaHEyuRp79uzhyJEjmX6fBgYG0qRJE/1tU4DFxsZiGAZBQUFWR5FscDqddOvWjVdeeYVatWpZHUcuw83qAJI3jh07hsPhIDQ0NNPy0NBQtm7dalEquVpOp5M+ffrQokULateubXUcyaaJEycSERHBmjVrrI4iV2H37t2MGjWKvn378vrrr7NmzRp69eqFh4cH3bt3tzqeZEP//v2Ji4ujevXq2O12HA4H7777Lg8//LDV0eQqHDlyBCDLv23OrJOCJTk5mX79+vHQQw8REBBgdRzJhvfffx83Nzd69epldRTJBhX/IgVQz5492bRpE0uXLrU6imTTgQMH6N27N3PmzMHLy8vqOHIVnE4nN954I++99x4ADRo0YNOmTYwePVrFfwExadIkxo0bx/jx46lVqxbr1q2jT58+lCxZUu+hiMXS0tJ44IEHME2TUaNGWR1HsmHt2rV89tlnREREYBiG1XEkG9Tt/zpRrFgx7HY7UVFRmZZHRUURFhZmUSq5Gs8//zx//fUXCxYsoHTp0lbHkWxau3Yt0dHRNGzYEDc3N9zc3Fi0aBEjRozAzc0Nh8NhdUS5jBIlSlCzZs1My2rUqMH+/fstSiRX6pVXXqF///48+OCD1KlTh27duvHiiy8ydOhQq6PJVTjz94v+tin4zhT++/btY86cObrqX0AsWbKE6OhoypYtm/G3zb59+3jppZcoX7681fEkCyr+rxMeHh7ccMMNzJs3L2OZ0+lk3rx5NGvWzMJkkl2mafL8888zefJk5s+fT4UKFayOJFegbdu2bNy4kXXr1mV83HjjjTz88MOsW7cOu91udUS5jBYtWlwwveb27dspV66cRYnkSiUmJmKzZf7Tx26343Q6LUok16JChQqEhYVl+tsmLi6OVatW6W+bAuRM4b9jxw7mzp1L0aJFrY4k2dStWzc2bNiQ6W+bkiVL8sorrzBr1iyr40kW1O3/OtK3b1+6d+/OjTfeSOPGjfn00085deoU//vf/6yOJtnQs2dPxo8fz5QpU/D398+4nzEwMBBvb2+L08nl+Pv7XzA+g6+vL0WLFtW4DQXEiy++SPPmzXnvvfd44IEHWL16NV9//TVff/211dEkm+644w7effddypYtS61atfj333/5+OOPefzxx62OJheRkJDAzp07Mx7v2bOHdevWERwcTNmyZenTpw/vvPMOVapUoUKFCrz55puULFnykqPJS9661HtYokQJ7rvvPiIiIvjrr79wOBwZf98EBwfj4eFhVWw57XI/g+efrHF3dycsLIxq1arldVTJDlOuK59//rlZtmxZ08PDw2zcuLG5cuVKqyNJNgFZfowZM8bqaHKVWrdubfbu3dvqGHIFpk2bZtauXdv09PQ0q1evbn799ddWR5IrEBcXZ/bu3dssW7as6eXlZVasWNEcMGCAmZKSYnU0uYgFCxZk+X9f9+7dTdM0TafTab755ptmaGio6enpabZt29bctm2btaElk0u9h3v27Lno3zcLFiywOrqYl/8ZPF+5cuXMTz75JE8zSvYZpmmaeXSeQUREREREREQsoHv+RURERERERAo5Ff8iIiIiIiIihZyKfxEREREREZFCTsW/iIiIiIiISCGn4l9ERERERESkkFPxLyIiIiIiIlLIqfgXERERERERKeRU/IuIXIcMw+DPP//MeLx161aaNm2Kl5cX9evXv+gyyez81/FyHnvsMTp37pxrec5Vvnx5Pv3000u2efvtt6/qvf3zzz+pXLkydrudPn36MHbsWIKCgq4qZ3bt3bsXwzBYt25drj7Plb6nVkhMTOTee+8lICAAwzA4efKk1ZGyxerXduHChZler7z4vhURyU9U/IuIFBKPPfYYhmFgGAbu7u6EhobSvn17vv/+e5xOZ6a2kZGRdOzYMePxwIED8fX1Zdu2bcybN++iy6yUnWJWzlqzZg1PP/10xuOcLLyeeeYZ7rvvPg4cOMCQIUPo0qUL27dvz5F9y+X98MMPLFmyhOXLlxMZGUlgYGCW7VJTU/nggw+oV68ePj4+FCtWjBYtWjBmzBjS0tLyOLX1mjdvfsnXS0SksHOzOoCIiOScDh06MGbMGBwOB1FRUcycOZPevXvz22+/MXXqVNzcXL/2w8LCMm23a9cuOnXqRLly5S657Eqlpqbi4eFx1dvL1QsJCcmV/SYkJBAdHU14eDglS5bMWO7t7Z0rzycX2rVrFzVq1KB27doXbZOamkp4eDjr169nyJAhtGjRgoCAAFauXMlHH31EgwYNrrsePR4eHhf87hMRuZ7oyr+ISCHi6elJWFgYpUqVomHDhrz++utMmTKFGTNmMHbs2Ix2514FNgyDtWvXMnjwYAzD4O23385yGcCBAwd44IEHCAoKIjg4mLvuuou9e/dm7PdMt/Z3332XkiVLUq1atSva7qOPPqJEiRIULVqUnj17ZlydbNOmDfv27ePFF1/M6N1wMYZh8NVXX3H77bfj4+NDjRo1WLFiBTt37qRNmzb4+vrSvHlzdu3alWm7UaNGUalSJTw8PKhWrRo//fRTpvU7duzgpptuwsvLi5o1azJnzpwLnvtyx3m+3377jTp16uDt7U3RokVp164dp06dyrLtjTfeyEcffZTxuHPnzri7u5OQkADAwYMHMQyDnTt3Apl7SpQvXx6Au+++G8MwMh6f8dNPP1G+fHkCAwN58MEHiY+PzzLDwoUL8ff3B+CWW27BMAwWLlyYqfu0aZq0a9eO8PBwTNME4Pjx45QuXZq33norY1/ffvstNWrUwMvLi+rVqzNy5MhMz7V69WoaNGiAl5cXN954I//+++9FX0eA119/nSZNmlywvF69egwePBhw9YZo3749xYoVIzAwkNatWxMREXHRfZ7fTRxg3bp1GIaR6X1dunQprVq1wtvbmzJlytCrV69M7+PIkSOpUqUKXl5ehIaGct99913yWH7//Xdq1aqFp6cn5cuXZ/jw4Rnr2rRpw/Dhw1m8eDGGYdCmTZss9/Hpp5+yePFi5s2bR8+ePalfvz4VK1aka9eurFq1iipVqmS5XUxMDA899BClSpXCx8eHOnXqMGHChExt2rRpQ69evXj11VcJDg4mLCws43fEGdn5eTmf0+lk6NChVKhQAW9vb+rVq8dvv/2Wsf7M+zF9+nTq1q2Ll5cXTZs2ZdOmTRlt9u3bxx133EGRIkXw9fWlVq1a/P3335m2v9RtEpf7PWAYBt9++y133303Pj4+VKlShalTp1722ERE8gVTREQKhe7du5t33XVXluvq1atnduzYMeMxYE6ePNk0TdOMjIw0a9WqZb700ktmZGSkGR8fn+Wy1NRUs0aNGubjjz9ubtiwwdy8ebPZtWtXs1q1amZKSkpGBj8/P7Nbt27mpk2bzE2bNmV7u4CAALNHjx7mli1bzGnTppk+Pj7m119/bZqmacbExJilS5c2Bw8ebEZGRpqRkZEXfR0As1SpUuYvv/xibtu2zezcubNZvnx585ZbbjFnzpxpbt682WzatKnZoUOHjG3++OMP093d3fzyyy/Nbdu2mcOHDzftdrs5f/580zRN0+FwmLVr1zbbtm1rrlu3zly0aJHZoEGDTK9jdo/zzHt0+PBh083Nzfz444/NPXv2mBs2bDC//PJLMz4+Psvj6tu3r9mpUyfTNE3T6XSawcHBZrFixcwZM2aYpmmaP//8s1mqVKmM9uXKlTM/+eQT0zRNMzo62gTMMWPGmJGRkWZ0dLRpmqY5cOBA08/Pz7znnnvMjRs3mosXLzbDwsLM119/PcsMKSkp5rZt20zA/P33383IyEgzJSXFHDNmjBkYGJjR7uDBg2aRIkXMTz/91DRN07z//vvNxo0bm2lpaRlZS5QoYf7+++/m7t27zd9//90MDg42x44da5qmacbHx5shISFm165dzU2bNpnTpk0zK1asaALmv//+m2W2TZs2mYC5c+fOC5bt2LHDNE3TnDdvnvnTTz+ZW7ZsMTdv3mw+8cQTZmhoqBkXF5exzbnv6YIFC0zAPHHiRMb6f//91wTMPXv2mKZpmjt37jR9fX3NTz75xNy+fbu5bNkys0GDBuZjjz1mmqZprlmzxrTb7eb48ePNvXv3mhEREeZnn32W5TGYpmn+888/ps1mMwcPHmxu27bNHDNmjOnt7W2OGTPGNE3Xz8JTTz1lNmvWzIyMjDRjYmKy3E/dunXNW2+99aLPczEHDx40P/zwQ/Pff/81d+3aZY4YMcK02+3mqlWrMtq0bt3aDAgIMN9++21z+/bt5g8//GAahmHOnj3bNM3s/bxk5Z133jGrV69uzpw509y1a5c5ZswY09PT01y4cKFpmmffjxo1apizZ882N2zYYN5+++1m+fLlzdTUVNM0TbNTp05m+/btzQ0bNpi7du0yp02bZi5atCjT9mfez/O/by/3e8A0Xd8fpUuXNsePH2/u2LHD7NWrl+nn53fR90FEJD9R8S8iUkhcqvjv0qWLWaNGjYzH5/8RXq9ePXPgwIGZtjl/2U8//WRWq1bNdDqdGctSUlJMb29vc9asWRkZQkNDM4rdK9muXLlyZnp6ekab+++/3+zSpUvG43OL2UsBzDfeeCPj8YoVK0zA/O677zKWTZgwwfTy8sp43Lx5c/Opp57KtJ/777/fvO2220zTNM1Zs2aZbm5u5qFDhzLWz5gxI9PrmN3jPPMerV271gTMvXv3XvaYTNM0p06dagYGBprp6enmunXrzLCwMLN3795mv379TNM0zSeffNLs2rVrRvvzX6+sCq+BAweaPj4+mYrfV155xWzSpMlFc5w4ccIEzAULFmQsO7+IMk3TnDRpkunl5WX279/f9PX1Nbdv356xrlKlSub48eMztR8yZIjZrFkz0zRN86uvvjKLFi1qJiUlZawfNWrUJYt/03R9zw4ePDjj8WuvvXbJY3E4HKa/v785bdq0jGVXWvw/8cQT5tNPP51pv0uWLDFtNpuZlJRk/v7772ZAQECm1/hSunbtarZv3z7TsldeecWsWbNmxuPevXubrVu3vuR+vL29zV69emXrOS+nU6dO5ksvvZTxuHXr1mbLli0ztWnUqFHG92J2fl7Ol5ycbPr4+JjLly/PtPyJJ54wH3roIdM0z74fEydOzFgfExNjent7m7/88otpmqZZp04d8+23387yOS5X/F/u94BpXvj7JSEhwQQyTsKJiORn6vYvInIdME3zkl3ls2P9+vXs3LkTf39//Pz88PPzIzg4mOTk5Exd6OvUqZPpPv/sblerVi3sdnvG4xIlShAdHX1VWevWrZvxdWhoaEauc5clJycTFxcHwJYtW2jRokWmfbRo0YItW7ZkrC9Tpkyme9ybNWuWqX12j/OMevXq0bZtW+rUqcP999/PN998w4kTJy56TK1atSI+Pp5///2XRYsW0bp1a9q0acPChQsBWLRo0UW7gF9K+fLlM7ryw7W97ue6//77ufvuuxk2bBgfffRRRjfzU6dOsWvXLp544omM18nPz4933nkn43XasmVLRrfuM85/vbPy8MMPM378eMD1PT9hwgQefvjhjPVRUVE89dRTVKlShcDAQAICAkhISGD//v1XfZzr169n7NixmY4lPDwcp9PJnj17aN++PeXKlaNixYp069aNcePGkZiYeNH9Xex7cceOHTgcjmznMk/fcnGlHA4HQ4YMoU6dOgQHB+Pn58esWbMueI3O/RmDzN832fl5Od/OnTtJTEykffv2mV7LH3/88YKfn3P3FRwcTLVq1TJ+Vnv16sU777xDixYtGDhwIBs2bMj2sV/u98AZ5x67r68vAQEBOfIzIyKS2zTgn4jIdWDLli1UqFDhmvaRkJDADTfcwLhx4y5Yd+7gcr6+vle1nbu7e6Z1hmFcMEtBdp27rzMnPbJadrX7z0p2j/MMu93OnDlzWL58ObNnz+bzzz9nwIABrFq1Ksv3KigoiHr16rFw4UJWrFhB+/btuemmmzJG2t+xYwetW7e+4tw5+bqfKzExkbVr12K329mxY0fG8jNjFHzzzTcX3KN/7smfq/HQQw/Rr18/IiIiSEpK4sCBA3Tp0iVjfffu3YmJieGzzz6jXLlyeHp60qxZM1JTU7Pcn83mukZybiF9/ij5CQkJPPPMM/Tq1euC7cuWLYuHhwcREREsXLiQ2bNn89Zbb/H222+zZs2aXJ1mrmrVqmzduvWKt/vwww/57LPP+PTTT6lTpw6+vr706dPngtcop79vznxfTJ8+nVKlSmVa5+npme39PPnkk4SHhzN9+nRmz57N0KFDGT58OC+88MJVZztfbv3MiIjkNl35FxEp5ObPn8/GjRu59957r2k/DRs2ZMeOHRQvXpzKlStn+rjU1FlXu935PDw8rujK55WoUaMGy5Yty7Rs2bJl1KxZM2P9gQMHiIyMzFi/cuXKTO2v5jgNw6BFixYMGjSIf//9Fw8PDyZPnnzRnK1bt2bBggUsXryYNm3aEBwcTI0aNXj33XcpUaIEVatWvei27u7uufb6ZeWll17CZrMxY8YMRowYwfz58wFXr4uSJUuye/fuC16nMyc9atSowYYNG0hOTs7Y3/mvd1ZKly5N69atGTduHOPGjaN9+/YUL148Y/2yZcvo1asXt912W8aAeseOHbvo/s6ctDn3fV+3bl2mNg0bNmTz5s0XHEvlypUzesC4ubnRrl07PvjgAzZs2MDevXszXo/zXex7sWrVqld0cqRr167MnTs3y4ES09LSLjqw5LJly7jrrrt45JFHqFevHhUrVrziaRyz8/Nyvpo1a+Lp6cn+/fsveB3LlCmTqe25+zpx4gTbt2+nRo0aGcvKlClDjx49+OOPP3jppZf45ptvsp37Ur8HREQKOhX/IiKFSEpKCkeOHOHQoUNERETw3nvvcdddd3H77bfz6KOPXtO+H374YYoVK8Zdd93FkiVL2LNnDwsXLqRXr14cPHgwx7c7X/ny5Vm8eDGHDh26ZMF2NV555RXGjh3LqFGj2LFjBx9//DF//PEHL7/8MgDt2rWjatWqdO/enfXr17NkyRIGDBhwTce5atUq3nvvPf755x/279/PH3/8wdGjRzMVMedr06YNs2bNws3NjerVq2csGzdu3GWv+pcvX5558+Zx5MiRS95ekBOmT5/O999/n1GAv/LKK3Tv3j3jeQcNGsTQoUMZMWIE27dvZ+PGjYwZM4aPP/4YcBWuhmHw1FNPsXnzZv7+++9MMx1cysMPP8zEiRP59ddfM3X5B6hSpQo//fQTW7ZsYdWqVTz88MOXnKLwTOH59ttvs2PHDqZPn55p5H2Afv36sXz5cp5//nnWrVvHjh07mDJlCs8//zwAf/31FyNGjGDdunXs27ePH3/8EafTmTETxvleeukl5s2bx5AhQ9i+fTs//PADX3zxRcb3Ynb16dOHFi1a0LZtW7788kvWr1/P7t27mTRpEk2bNs3UG+P81+hMj5QtW7bwzDPPEBUVdUXPnZ2fl/P5+/vz8ssv8+KLL/LDDz+wa9cuIiIi+Pzzz/nhhx8ytR08eDDz5s1j06ZNPPbYYxQrVozOnTtnHPesWbPYs2cPERERLFiw4JI/U+e63O8BEZGCTsW/iEghMnPmTEqUKEH58uXp0KEDCxYsYMSIEUyZMuWau1T7+PiwePFiypYtyz333EONGjV44oknSE5OJiAgIMe3O9/gwYPZu3cvlSpVyvE57Dt37sxnn33GRx99RK1atfjqq68YM2ZMxj30NpuNyZMnk5SUROPGjXnyySd59913M+3jSo8zICCAxYsXc9ttt1G1alXeeOMNhg8fTseOHS+as1WrVjidzkyFfps2bXA4HJe933/48OHMmTOHMmXK0KBBg+y/OFfo6NGjPPHEE7z99ts0bNgQcBX7oaGh9OjRA3B1zf72228ZM2YMderUoXXr1owdOzbjyr+fnx/Tpk1j48aNNGjQgAEDBvD+++9n6/nvu+8+YmJiSExMzCgIz/juu+84ceIEDRs2pFu3bvTq1StTz4Dzubu7M2HCBLZu3UrdunV5//33eeeddzK1qVu3LosWLWL79u20atWKBg0a8NZbb2Xc7x4UFMQff/zBLbfcQo0aNRg9ejQTJkygVq1aWT5nw4YNmTRpEhMnTqR27dq89dZbDB48mMceeyxbx3+Gp6cnc+bM4dVXX+Wrr76iadOmNGrUiBEjRtCrVy9q166d5XZvvPEGDRs2JDw8nDZt2hAWFnbB63g52fl5ycqQIUN48803GTp0KDVq1KBDhw5Mnz79gttghg0bRu/evbnhhhs4cuQI06ZNy+hl4XA46NmzZ8b2VatWvWAayYu53O8BEZGCzjCvdkQYEREREZE8snDhQm6++WZOnDiRq+MliIgUVrryLyIiIiIiIlLIqfgXERERERERKeTU7V9ERERERESkkNOVfxEREREREZFCTsW/iIiIiIiISCGn4l9ERERERESkkFPxLyIiIiIiIlLIqfgXERERERERKeRU/IuIiIiIiIgUcir+RURERERERAo5Ff8iIiIiIiIihZyKfxEREREREZFC7v82YbDzyBrTxwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.ioff()\n",
    "\n",
    "results_df = pd.DataFrame(gs_concrete.cv_results_)\n",
    "\n",
    "fig, ax = plt.subplots(1, figsize=(12, 8))\n",
    "(l1,) = ax.plot(\n",
    "    np.arange(16), -results_df.loc[results_df[\"param_n_bits\"] == 6, \"mean_test_score\"], \"-o\"\n",
    ")\n",
    "(l2,) = ax.plot(\n",
    "    np.arange(16), -results_df.loc[results_df[\"param_n_bits\"] == 8, \"mean_test_score\"], \"-o\"\n",
    ")\n",
    "(l3,) = ax.plot(\n",
    "    np.arange(16), -results_df.loc[results_df[\"param_n_bits\"] == 12, \"mean_test_score\"], \"-o\"\n",
    ")\n",
    "ax.legend((l1, l2, l3), (\"n_bits = 6\", \"n_bits = 8\", \"n_bits = 12\"), loc=\"upper right\", shadow=True)\n",
    "ax.set_xlabel(\"Different models with fixed values of C and epsilon\")\n",
    "ax.set_ylabel(\"Mean MSE accros CV folds\")\n",
    "ax.set_title(\"Impact of `n_bits` on Cross Validation performances\")\n",
    "display(fig)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9765fd9c",
   "metadata": {},
   "source": [
    "As seen in the graph, this fairly simple data-set has only a single feature and few points, in addition to a fairly simple model with only few parameters for the decision rule. The complexity of the information to be represented as integers is not huge. This results in `n_bits` values having less of an impact on the perfomance of the model. \n",
    "\n",
    "You can see that for the best model, performances with `n_bits` equal to `6`, `8` or `12` are quite close. Performances differ for models with `C = 100000.0`, meaning the `l2` penalty is weak and the decision rule can adjust more easily to the data. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "991386ee",
   "metadata": {},
   "source": [
    "### Compare sklearn and Concrete ML Quantized best models."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "512f94e0",
   "metadata": {},
   "source": [
    "#### performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e884473f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean time fit sklearn: 0.002s, std time fit sklearn: 0.002s\n",
      "Mean time fit concrete: 0.027s,std time fit concrete: 0.002s\n",
      "Best MSE score sklearn: 4069.06\n",
      "Best MSE score concrete: 4053.61\n"
     ]
    }
   ],
   "source": [
    "# Print mean time fit and std time fit for both models\n",
    "print(\n",
    "    f\"Mean time fit sklearn: {np.mean(gs_sklearn.cv_results_['mean_fit_time']):.3f}s,\"\n",
    "    f\" std time fit sklearn: {np.std(gs_sklearn.cv_results_['mean_fit_time']):.3f}s\"\n",
    ")\n",
    "print(\n",
    "    f\"Mean time fit concrete: {np.mean(gs_concrete.cv_results_['mean_fit_time']):.3f}s,\"\n",
    "    f\"std time fit concrete: {np.std(gs_concrete.cv_results_['mean_fit_time']):.3f}s\"\n",
    ")\n",
    "\n",
    "# Print best score for both models\n",
    "print(f\"Best MSE score sklearn: {-gs_sklearn.best_score_:.2f}\")\n",
    "print(f\"Best MSE score concrete: {-gs_concrete.best_score_:.2f}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "03f0143f",
   "metadata": {},
   "source": [
    "#### hyperparameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7d5027bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters for Concrete: {'C': 10000.0, 'epsilon': 20.0, 'n_bits': 6}\n",
      "Best parameters for Sklearn: {'C': 10000.0, 'epsilon': 20.0}\n"
     ]
    }
   ],
   "source": [
    "# Get best hyperparameters out of gs_concrete\n",
    "best_params_concrete = gs_concrete.best_params_\n",
    "print(f\"Best parameters for Concrete: {best_params_concrete}\")\n",
    "best_params_sklearn = gs_sklearn.best_params_\n",
    "print(f\"Best parameters for Sklearn: {best_params_sklearn}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "326ef351",
   "metadata": {},
   "source": [
    "### train with best hyperparameter set on the complete training data-set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "23d61559",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train concrete and sklearn LinearSVR with best hyper parameters\n",
    "concrete_rgs = ConcreteLinearSVR(**best_params_concrete)\n",
    "\n",
    "concrete_rgs, sklearn_rgs = concrete_rgs.fit_benchmark(X_train, y_train)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d9251a9d",
   "metadata": {},
   "source": [
    "## Concrete ML Quantized LinearSVR with FHE"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "64dcbfc0",
   "metadata": {},
   "source": [
    "### Prerequisite."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "8eba8bb8",
   "metadata": {},
   "source": [
    "Some prerequisites should be reviewed before you dive in.\n",
    "\n",
    "Quantization is a technique that converts continuous data (floating point, e.g., in 32-bits) to discrete numbers within a fixed range (in our case either 6, 8, or 12 bits). This means that some information is lost during the process. However, the larger the integers' range, the smaller the error becomes, making it acceptable in some cases.\n",
    "\n",
    "To learn more about quantization, please refer to this [documentation section](../explanations/quantization.md).\n",
    "\n",
    "Regarding FHE, the input data type must be represented exclusively as integers, making the use of quantization necessary. A linear model trained on floats is quantized into an equivalent integer model using *Post-Training Quantization*. This operation can lead to a loss of accuracy compared to the standard floating point models working on clear data.\n",
    "\n",
    "In practice, this loss is usually very limited with linear FHE models as they can consider very large integers with up to 50 bits in some cases. This means these models can quantize their inputs and weights over a large number of bits while still considering data-sets containing many features (e.g. 1000). We often observe almost identical performance scores between float, quantized, and FHE models.\n",
    "\n",
    "To learn more about the relation between the maximum bit-width reached within a model, the bits of quantization used, and the data-set's number of features, please refer to this [documentation section](../explanations/pruning.md)."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "80e835f8",
   "metadata": {},
   "source": [
    "### Compilation."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "0ea0e874",
   "metadata": {},
   "source": [
    "To perform homomorphic inference, we take the above trained quantized model and we compile it to get an FHE model.\n",
    "\n",
    "The compiler requires an exhaustive set of data to evaluate the maximum integer bit-width within the graph, which is needed during FHE computations before running any predictions.\n",
    "\n",
    "You can either provide the entire trained data-set or a smaller but representative subset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c9590403",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the model using the training data\n",
    "circuit = concrete_rgs.compile(X_train)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b33827f6",
   "metadata": {},
   "source": [
    "### Generate the key."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b00a9172",
   "metadata": {},
   "source": [
    "The compiler returns a circuit, which can then be used for key generation and predictions. More precisely, it generates:\n",
    "\n",
    "* a Secret Key, used for the encryption and decryption processes. This key should remain accessible only to the user.\n",
    "* an Evaluation Key, used to evaluate the circuit on encrypted data. Anyone can access this key without breaching the model's security.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "53cc3b51",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating a key for an 7-bit circuit\n"
     ]
    }
   ],
   "source": [
    "# Generate the key\n",
    "print(f\"Generating a key for an {circuit.graph.maximum_integer_bit_width()}-bit circuit\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3212b1b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Key generation time: 0.00 seconds\n"
     ]
    }
   ],
   "source": [
    "time_begin = time.time()\n",
    "circuit.client.keygen(force=False)\n",
    "print(f\"Key generation time: {time.time() - time_begin:.2f} seconds\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "752452bc",
   "metadata": {},
   "source": [
    "### Now let's predict using the FHE model on encrypted data."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9b71e63e",
   "metadata": {},
   "source": [
    "Please notice `fhe=\"execute\"`, which creates the job under the hood: Before the data is sent to be applied to the model, it is encrypted with the client secret key, generated above. \n",
    "\n",
    "As for future comparison below, we also predict on the very same data on both:\n",
    "* the sklearn model;\n",
    "* the Concrete ML quantized model without FHE. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ccc0edcc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time: 0.0026 seconds per sample\n",
      "Execution time: 0.0000 seconds per sample\n",
      "Execution time: 0.0000 seconds per sample\n"
     ]
    }
   ],
   "source": [
    "# Now predict using the FHE-quantized model on the testing set\n",
    "time_begin = time.time()\n",
    "y_pred_fhe = concrete_rgs.predict(X_test, fhe=\"execute\")\n",
    "print(f\"Execution time: {(time.time() - time_begin) / len(X_test):.4f} seconds per sample\")\n",
    "\n",
    "# Now predict using the Sklearn model on the testing set\n",
    "time_begin = time.time()\n",
    "y_pred_sklearn = sklearn_rgs.predict(X_test)\n",
    "print(f\"Execution time: {(time.time() - time_begin) / len(X_test):.4f} seconds per sample\")\n",
    "\n",
    "# Now predict using clear quantized Concrete ML model on testing set\n",
    "time_begin = time.time()\n",
    "y_preds_quantized = concrete_rgs.predict(X_test)\n",
    "print(f\"Execution time: {(time.time() - time_begin) / len(X_test):.4f} seconds per sample\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d7e99e85",
   "metadata": {},
   "source": [
    "## Compare"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d8d8c76d",
   "metadata": {},
   "source": [
    "### Display performance spreads."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5dbc0d68",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clear FP32 sklearn model MSE: 3852.260\n",
      "Clear quantized model MSE: 3846.509\n",
      "FHE model MSE: 3846.509\n",
      "\n",
      "Relative difference between Concrete-ml (quantized clear) and Concrete-ml (FHE) scores: 0.00%\n",
      "Relative difference between scikit-learn (clear) and Concrete-ml (FHE) scores: 0.15%\n"
     ]
    }
   ],
   "source": [
    "# Print all MSE a string to explain\n",
    "\n",
    "mse_sklearn = mean_squared_error(y_test, y_pred_sklearn)\n",
    "mse_clear = mean_squared_error(y_test, y_preds_quantized)\n",
    "mse_fhe = mean_squared_error(y_test, y_pred_fhe)\n",
    "\n",
    "print(\n",
    "    f\"Clear FP32 sklearn model MSE: {mse_sklearn:.3f}\\n\"\n",
    "    f\"Clear quantized model MSE: {mse_clear:.3f}\\n\"\n",
    "    f\"FHE model MSE: {mse_fhe:.3f}\"\n",
    ")\n",
    "\n",
    "# Measure the error of the FHE-quantized model with respect to quantized clear Concrete ML model\n",
    "concrete_score_difference = abs(mse_fhe - mse_clear) * 100 / mse_clear\n",
    "print(\n",
    "    \"\\nRelative difference between Concrete-ml (quantized clear) and Concrete-ml (FHE) scores:\",\n",
    "    f\"{concrete_score_difference:.2f}%\",\n",
    ")\n",
    "\n",
    "\n",
    "# Measure the error of the FHE quantized model with respect to the sklearn float model\n",
    "score_difference = abs(mse_fhe - mse_sklearn) * 100 / mse_sklearn\n",
    "print(\n",
    "    \"Relative difference between scikit-learn (clear) and Concrete-ml (FHE) scores:\",\n",
    "    f\"{score_difference:.2f}%\",\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "18786764",
   "metadata": {},
   "source": [
    "We can observe that scikit-learn and Concrete ML (quantized clear) models output **very close** scores. This demonstrates how the quantization process has a very limited impact on performances.\n",
    "\n",
    "We can observe as well that the performance difference between Concrete ML (quantized clear) and Concrete ML (FHE) is quasi null. This demonstrates that the compilation process has a very limited impact on performance. If one observed a more significant difference, then `n_bits` can be increased to offer more degrees of freedom during the compilation process. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "87023b7e",
   "metadata": {},
   "source": [
    "### Visualize the decision rule."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "205ae5c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We densify the space representation of the original X,\n",
    "# to better visualize the resulting step function in the following figure\n",
    "x_space = np.linspace(X_test.min(), X_test.max(), num=300)\n",
    "x_space = x_space[:, np.newaxis]\n",
    "y_pred_q_space = concrete_rgs.predict(x_space)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1825a31e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.ioff()\n",
    "\n",
    "plt.clf()\n",
    "fig, ax = plt.subplots(1, figsize=(12, 8))\n",
    "fig.patch.set_facecolor(\"white\")\n",
    "ax.scatter(X_train, y_train, **train_plot_config)\n",
    "ax.scatter(X_test, y_test, **test_plot_config)\n",
    "ax.plot(X_test, y_pred_sklearn, **get_sklearn_plot_config(mse_sklearn))\n",
    "ax.plot(x_space, y_pred_q_space, **get_concrete_plot_config(mse_clear))\n",
    "ax.legend()\n",
    "display(fig)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1461c97b",
   "metadata": {},
   "source": [
    "In the above graph, you can see that the test data-set has a point for which `X` value is outside the range of the trained data-set. Since, when we compiled the quantized model we used `X_train`, this `X` value in the test data-set was not seen by the compilation process, the decision rule poorly generalizes on those values outside the boundaries observed on `X_tain`. An alternative to correct this would be to give at model compilation time, a wider range of values for `X`. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "141bc829",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c44fff29",
   "metadata": {},
   "source": [
    "We have shown how easy it is to train and execute a LinearSVR regression model in FHE using Concrete ML.\n",
    "\n",
    "We have also discussed the development flow of an FHE model: training, quantization, compilation, and inference.\n",
    "\n",
    "Prediction performances are quasi identical. The tiny difference is explained by the quantization process and the compilation of the trained model. "
   ]
  }
 ],
 "metadata": {
  "execution": {
   "timeout": 10800
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
